All copying, partial or complete, by any process ... · use of CRISPR-Cas9, the famous molecular scissors which will make it possible to copy and paste genomes. As a philosopher in

All copying, partial or complete, by any process whatsoever, is forbidden without prior written authorization from the editor and authors.

ISBN 978-2-9558316-1-8

Artificial intelligence:

dream or nightmare for radiologists?

4 - Artificial Intelligence: dream or nightmare for radiologists

CONTENTS Preface p 06

Dr. Alain François p 08

Dr. Jean-Philippe Masson p 09

Foreword p 11

Jean-Michel Besnier: Will the effectiveness of artificial intelligence impose heteronomy on humans? p 16

Paul Chang: Artificial intelligence in the world of radiology p 26

Luc Soler: Artificial intelligence in medical imaging: hope or Pandora’s Box? p 44

David Gruson: Ethics in the development of Artificial intelligence p 54

Jacques Lucas: Artificial intelligence and medicine: the official recommendations of

the Medical Board p 66

Robert Lavayssière: How can private practice radiologists prepare for the

advent of Artificial Intelligence? p 76

Jean-François Meder: What does the SFR think Artificial intelligence will change for radiology? p 90

Cédi Koumako: How can radiology residents prepare for the advent of Artificial Intelligence? p 96

Laure Soulier: Who owns radiological data in the framework of big data? p 104

Florent Parmentier: How Artificial Intelligence in healthcare

became a geopolitical reality p 112

François Blanchardon: Will Artificial Intelligence change the patient-radiologist relationship? p 124

I - OPENING

II - TALKS

Contents - 5

What manufacturers think:

Aïssa Khélifa: AGFA p 134

François Vorms: CANON p 140

Christophe Lala: GE p 144

Antonin Marcault: PHILIPS p 150

Jean-Baptiste Leprince: SIEMENS HEALTHINEERS p 154

Open debate p 160

Round table discussion p 176

A French artificial intelligence ecosystem p 196

Press release: G4 June 2018 p 197

III - DISCUSSIONS

IV - A FRENCH AI ECOSYSTEM

V - SPEAKERS p 198

6 - Artificial Intelligence: dream or nightmare for radiologists

Foreword

Every spring, the French Radiologists Union (FNMR) organizes a discussion seminar on a subject, the development of which could change the future of radiology.

Artificial intelligence (AI) was initially presented in the October 2017 issue of the review Médecin Radiologue published during the Francophone Radiology Days (JFR).

The FNMR decided to participate in this movement and to pursue the discussion during the FNMR seminar scheduled for June 2018 in Lyon.

The approach to artificial intelligence desired by the FNMR was to step outside the box to try to grasp what the repercussions were on various levels: philosophical, ethical, deontological, etc.

Therefore, the decision was made to unite experts from these different spheres who are highly involved in the fields, for this discussion day.

This brochure is a re-transcription of the experts' talks and the discussions that took place that day.

At the end of the day, on behalf of the French Council of Professional Radiology (CNPR), Prof. François Meder and I announced the launch of a French artificial intelligence ecosystem dedicated to medical imaging.

Artificial intelligence is a new challenge for radiologists.

The seminar in Lyon is only a first step.

Dr. Jean-Philippe MASSON, President of the French Radiologists Union.

Preface - 7

8 - Artificial Intelligence: dream or nightmare for radiologists?

I - OPENING

Dr Alain FRANCOIS, President of the Rhône-Alpes Regional Radiologists Union

First of all, I’d like to say welcome, but most of all, thank you very much. My heartfelt thanks go out to our many partners for participating in the organization of the seminar under very satisfactory working conditions and in a convivial atmosphere.

It is not unusual for the subject of this seminar to attract this massive participation because in principle, artificial intelligence will be of interest to the entire imaging chain from data production to data transfer, storage, processing and of course, all that this will entail in terms of changes in our practices, our organizations and the respective responsibilities. But, I won't spoil things by talking about the different subjects that will be broached today. So, I'll hand over to Jean-Philippe Masson.

Chapter I - Opening - 9

Dr. Jean-Philippe MASSON, President of the French Radiologists Union

First of all, thank you very much Alain François and the AURA region for organizing this seminar.

You know the FNMR seminars. We always try to think ahead. We always try to anticipate the changes in our profession. In 2017, we held a seminar on pertinence. It inspired us through discussions and the signing of the summary of conclusions with the Health Insurance.

In 2017 also, but in the United States, artificial intelligence was a major subject at the RSNA. It was important to launch the discussion in France although certain naysayers are predicting that in ten years there will be no more need for radiologists. We are radiologists. We know the meaning of radiology and what patients expect from us.

That is why we launched this discussion which is not limited to the scientific and technical aspect. It opens up to explore a more philosophical dimension, and even the implication of artificial intelligence in everyday life on an ethical or deontological level, which is why the Medical Board is here, or on a geopolitical level, because it concerns information technology, therefore, it will largely exceed our boundaries.

I would like to thank all the participants for agreeing to play along, notably Paul Chang who came all the way from Chicago.

Good work.


Olivier COLIN, mediator

Mr. President, thank you. Today, I will take on the role of a policeman to ensure that our twenty experts have the time to share their knowledge with us.

One of these experts is Professor Paul Chang from Chicago who accepted the invitation of the President of the FNMR. Chicago is the Mecca of radiology. Everyone knows their prestigious union, the RSNA 1. Let's give Mr. Chang a standing ovation because it isn’t every day that speakers cross the Atlantic for us.

Is artificial intelligence a dream or a nightmare for radiologists? In reality, even though we are talking about it a lot today, it is first and foremost a fantasy that started in the 1950s with the human brain. The fantasy was to mime the very functioning, or even sublimate the human brain. Several years ago, it happened. Remember, twenty years ago, an artificial intelligence tool, Deep Blue, was able to beat the best chess player in the world. It was Garry Kasparov.

Over the past decade, there has been an exponential development in artificial intelligence. This artificial intelligence is present in many applications. It's in your telephones, connected objects and your cars. Obviously, health is one of the major issues.

It would appear that Watson has become the dream assistant for many doctors. The digital patient is actually already a reality. Running a practice based on prevention, sharing, participation and most of all, prediction is possible because of artificial intelligence.

What is the impact of this artificial intelligence on your profession? As the President said earlier, there are two opposing prophecies. One is that in ten years you will no longer be here. But there is also the possibility that enhanced radiology imposes transhumanism on Humans. That is what artificial intelligence may transform in your profession to ensure that your specialization is even more essential than it is at present. I prefer to start off on this second hypothesis. n

Deep Blue beat Garry Kasparov 1 Radiological Society of North America

Chapter I - Notice - 11

Notice: The following articles are copies of talks by experts who participated in the seminar. This explains the colloquial and direct quality of the re-transcriptions.

II - Talks

Jean-Michel BESNIER

Emeritus Professor of Philosophy - Sorbonne-University. Manager of the Research Center "Connected

health, enhanced humans". Institute of Communication

Sciences - CNRS

Will the effectiveness of

artificial intelligence impose heteronomy on

humans?


I

II - TALKS

Will the effectiveness of Artificial Intelligence impose heteronomy on humans? *

t is a difficult task to open a day on a question that is formulated in such a dramatic way. Artificial

intelligence, dream or nightmare for radiologists? You first suggested that I give my talk the title: "Who will win power, radiologists or robots?" I admit, I was daunted by the magnitude of a question that seemed to be beyond my competence. So, I changed the title, but I am still not quite happy with it: "Will the effectiveness of Artificial Intelligence impose heteronomy on humans?" I realized that the title was also quite coded. Please scrap it if you want to.

* Re-transcription of the talk

It would probably be simpler if I start by explaining how I come to be giving a talk to an audience of radiologists such as this one. I, who am not even a doctor but let's say, a philosophy professor.

Let me say very quickly that for about two decades I've been interested in the cognitive sciences and artificial intelligence, but from an epistemological standpoint. I belonged to an

École Polytechnique laboratory called the Center for Applied Epistemological Research, where we examined the impact of the cybernetics that came about at the end of the 1950s and which was the basis of the dream of an artificial organism. More precisely, I wondered about the way in which the cognitive sciences and artificial intelligence could cast a new vision on the major questions

Chapter II - Will the effectiveness of artificial intelligence impose heteronomy on humans? - 17

of time immemorial faced by philosophy. Obviously, it meant knowing the actual nature of intelligence. I also wondered to what extent the old question of the soul/body relationship still had a meaning and tried to understand the dynamics of individual and collective emotions; for example, the emergence of conventions in societies.

The questions were very diverse, but gradually, this questioning got me interested in what Olivier Colin just mentioned, transhumanism. As you know, transhumanism claims that cognitive sciences and artificial intelligence combined with the neurosciences, nanotechnologies, the information sciences and biotechnologies, this group that is now called NBIC 1, could fulfil all the aspirations of the human race. This is the transhumanists' main premise, especially those from Silicon Valley. By converging, these four disciplines are in the process of finally preparing a situation in which all the aspirations of the human race could be fulfilled. I won't go into the details. It would be too long. It’s almost laughable. The proposal, the main promise is quite simply to kill death according to the slogan that Calico, the company created by Google, has adopted as their motto.

If you really want to have a first-hand understanding of what is transhumanism, you must read the report submitted to the American government in 2003, entitled: "Converging Technologies for Improving Human Performances". It can be found on the Web and is a 400-page report that starts with the words "We stand at the threshold of a new renaissance". I should point out that one of the writers of this report which condenses almost all the promises made by each of the disciplines in the NBIC, is a religious historian.

I made it a rule to examine the credibility of these transhumanist prophets as closely as possible. One example is the credibility of the famous Mind uploading, which involves uploading the content of the brain to a silicon chip or to other inalterable materials and possibly, into cyberspace. I was interested in it because it is one of the recipes for immortality for most transhumanists. Obviously, I was also interested in

1 Nanotechnologies, biotechnologies, information technology and cognitive sciences


perspectives opened by IPS, induced pluripotent stem cells, because they are the promise of continuous regeneration of organs, but possibly also one of the factors of this immortality that is offered. Like many others, I'm interested in the use of CRISPR-Cas9, the famous molecular scissors which will make it possible to copy and paste genomes. As a philosopher in any case, all this seemed worthy of my interest. Nevertheless, my approach is not very different from that of sociologists because I wonder, less, I must admit, about the feasibility of all these promises. I am not against futurologists who truly question the feasibility of these technologies. I am more interested in the reasons for the success of the transhumanists' announcements in a wider audience. Over the past ten years at least, I have seen the magnitude that the subject is taking on today in the media and in people's minds.

I have come to the conclusion that the Trojan horse of transhumanism is really health care. Transhumanism enters the mind of our contemporaries through the question of health care and therefore, to a certain extent, it puts you in the front line.

Medicine is held hostage by the techno-prophets who, paradoxically, at the same time announce that it is going to disappear. You mentioned Laurent Alexandre. Laurent Alexandre is one of the people with whom I have regular conversations. We both wrote a discussion book of arguments entitled Les robots font-ils l'amour? (Do Robots make Love?). It is in this book that we argue the loudest.

In any event, I had to come to the realization that in light of the traps for the

unwary in connected medicine, and which would therefore be predictive and personalized, in light of this trap, the threats brandished concerning technology that would deprive us of our initiatives and

which would dehumanize us are not valid. In fact, I realize that finally, when you tell people, "You have ankle monitors with biosensors, with all these elements that will obviously make you completely hypochondriacal because you self-monitor continuously, but with all this you will gain 30, 50 or 100 years of longevity, etc." they are ready to give anything for that. From a philosophical standpoint,


it is very difficult. We are far behind these techno-prophets.

And yet, the signs are there. You probably saw the OCDE 2 report at the beginning of the year which revealed that only 13% of all activities are likely to resist artificial intelligence. That means 87% of our activities are automatable and consequently, can be more effectively entrusted to machines. Maybe you’re familiar with the CNIL3 report which was also published two or three months ago, entitled: "Jusqu'où allons-nous perdre les commandes?" (How much will we lose control?) That venerable commission is also alarmist to a certain degree. Maybe you recall the appeal signed by Stephen Hawking and which was also signed by Bill Gates, Elon Musk and everyone who is considered important in the artificial intelligence world. That appeal was entitled: "Artificial intelligence is likely to destroy the human race". This is quite a dramatic title. We’re faced with a sort of paradox. Technology should contribute to the triumph of human autonomy and more and more it seems like it will subjugate us. That is the paradox and hence the title of my talk.

For someone who practices philosophy, Descartes or Condorcet already considered medicine as the field in which humans would find their emancipation. Descartes said "In the short term, medicine should make it possible to live 100 years". That was his goal.

In 1793, Condorcet went much further. He said "Basically, there is no need to shy away from the idea that maybe we will be able to become immortal through sciences and techniques". At present, we're being told something similar, but through the revelation that maybe we have the technological means to do so and that in addition, basically the future will certainly be without us and that the fraction of humanity that will benefit from enhancement technologies will finally have had to relinquish being human. It will be post-human. It will have merged with machines, with those machines that it would have made, obviously. It will find recourse in those connected objects that the market will have more and more to offer. One of the latest announcements in that domain is the one made by Elon Musk who created the company called Neuralink, which intends to develop those connected objects.

We’re witnessing a kind of anthropological mutation that NBIC technologies are preparing and which, sometimes give rise to quite legitimate questions. I can count the few major ethical questions that will be mentioned again at the end of the morning, certainly: isn't the organism just an assembly of organs that need to be maintained, repaired, changed, transformed and amplified? Aren't we just gene containers to be exploited to the maximum or optimized by genomic means? Aren't we just media for data

2 Organisation de Coopération et Développement Economiques (Economic Cooperation and Development Agency) 3 Commission Nationale de l’Informatique et des Libertés (French Data Protection Agency)


to be collected and correlated in order to adapt the offers for use, obviously aimed at fulfilling ideas of well-being? This is the very well-being with which health is associated because it is the very definition of it. Does the notion of human dignity still have a meaning when we insist on treating the digital patient like a metabolic system capable of being technologically perpetuated? Therefore, aren't we just gesticulating beings similar to animals and machines and not beings of speech and signs, capable of exchanging dialogue? You see, these are major questions. Maybe they are high school philosophy questions, but they are timeless questions.

These questions and many more comprise the critical argument against the blind faith that some people put in the algorithms of artificial intelligence. At present, artificial intelligence is the general name given to a sort of de-symbolization of humans. Humans reduced to their basic mechanisms, if you wish, simplified humans who seem to support their submission to machines of all sorts. Therefore, artificial intelligence is presented as inevitable. It is like the fourth narcissistic wound of humanity. Freud said that "Over the course of time, humans have suffered three major narcissistic blows. They thought the earth was the center of the universe. Galileo made them realize it was only a tiny speck. They thought they had descended from God. Darwin taught

them that they were much closer to apes. We thought we were perfectly conscious, responsible and rational and Freud explained that the unconscious is more important than the conscious, and this was the third blow. The fourth blow is actually the realization that the machines we have created have outwitted us today and will soon relegate us among the chimpanzees of the future. Obviously, this is all very dramatic.

So, can I now venture to speak in front of doctors, in front of you with some pertinence? I think I’m talking to doctors who have yet to assimilate this narcissistic blow. Undoubtedly, general practitioners whose practice was essentially comprised of auscultations, clinical relations and dialogues with their patients, those GPs know that right away they’re snowed under. In a way, I'm sorry to say so, but you play a role in their disappearance, because the imaging you produce and exploit constitutes progress that has considerably upset the clinical relationship which was essentially based on dialogue. What is assumed as obvious in "seeing" makes "listening to suffering", the very formulation of words of woes, of secondary importance as one of my colleagues, Odile Marcel, says.

It might also be said that treatment prescriptions, limited appointment time, etc. have increased the patient's feeling that they are not interesting and that they no longer deserve the doctor's attention,


except through test results and MRIs in between. Maybe the way I say this is a bit provocative, but the disappearance of traditional family medicine is experienced on a day-to-day basis by people who often find it regrettable that they are only considered as an image and an image that is finally decoded or disconnected from speech.

You radiologists know that very well. You are at the heart of the ambivalence that characterizes so-called connected medicine. You are perceived as the crossroads between medicine and signal processing engineering. Your promises are notorious. The collection of 3D images with tomographic synthesis, fusion of images with dynamic ultrasound virtualization, the means that you have to produce tele-imaging, all this makes you, don't take it badly, really the lair of virtual technology. Your physical distance from the patient seems functionally inevitable. From the standpoint you are given, the relationship that consists of conversing with him is not necessary. Conversation cannot influence the observation intended for the diagnosis. People still have difficulty acknowledging that imaging is a resource, as much for diagnosis as for the therapeutic process itself, for example, to evaluate the effectiveness of anti-tumor treatment at the cell level. Radiology is perceived as an arbitrary specialization, and yet it concerns the image that could be the most concrete. That is the paradox.

Whether or not you want it, your activities are among those that are considered to be automatable as the OCDE report I referred to a moment ago mentions.

It is said that your specialization is particularly threatened by artificial intelligence. That is what Laurent Alexandre says. Under normal circumstances, yours will be the first discipline to be swept aside. Urology has already been swept aside, I think. Maybe, it is also because you seem to be an exception to the rules in the regular sense. I'm saying it in this way because Cédric Villani, who recently submitted a report on artificial intelligence, said in an almost anecdotal way that machines will remain incapable of handling situations that require common sense solutions.


No doubt the expression 'common sense' in this context means that which is related to intuition, spontaneous inventiveness, opinions, that which is informal, etc. For a general practitioner, common sense is certainly related to the patient's subjective feelings, complaints, the psychological aspect, etc. Although radiologists are not confronted with this 'common sense', they do in fact, run the risk of being swallowed up by artificial intelligence which only needs modelling to simulate, scanners to extract more and more precise information, more and more data to learn and plan, etc. Therefore, radiologist's functional vulnerability could be due to the fact that these instruments push aside what is informal and that at the same time, they can rely on AI more and more to reach their full potential. It is AI that will recognize the subtlest shapes. It is AI that will reduce wrong interpretations, false positives and false negatives. It is AI that will guide surgical robotics by decoding and constructing images. Basically, the absence of humans could be the Achilles heel of radiology because it is humans who still prevent machines from being all powerful. The context is vast. When medicine is established as

a science of measurement, this is what it becomes more and more, and when it is established as algorithmic technology, -for decades now we have been saying that humans are above all algorithm-, we lay ourselves open to it dehumanizing humans. This does not mean it is less capable of repairing or extending its existence, but quite the contrary. If radiologists see themselves entirely in this option of quantified medicine, as is often said, it is not surprising that they are the first to be threatened by measuring instruments that are increasingly sophisticated. We could go even further and make a quasi-metaphysical incursion, put things in a somewhat cultural perspective to justify the worries which are not corporatist, but which are like the humanism of medicine confronted by the power of machines. Imagining the dematerialization of all things is deeply rooted in human cultures. All the major traditions place emphasis on this. It used to be the Orphic, Gnostic, Christian and Buddhist traditions. All these traditions associate human realization with its decorporalization. This type of imagining is now reinforced by virtual technologies which make it possible, for example, to simulate a brain or the functioning of an organ so that the digital patient who is benefiting from these technologies finds himself involved in the dehumanizing process that transhumanism assumes is the only end to humanity. In general, that is what it is for transhumanists. It is the fusion with machines and dematerialization that will enable us to save ourselves if we talk in terms of salvation in the religious sense of the term.


This last comment is obviously too suggestive and not adequately supported by argument. It is asking yourself whether resisting the transhumanist prophesies doesn't mean reconciling with humans, with human flesh, and whether physicians in general do not have a role to play in this reconciliation by putting forward the argument that what is in humans cannot be reduced to calculation. Medicine in general might have to form a united front against the pretension of hegemony which is the distinctive feature of connected medicine. Obviously, this doesn't mean denying the benefits of artificial intelligence, but rather refusing that it de-symbolize the patient, as it does when it treats patients only in terms of their genes, organs, and metabolism.

So, this brings me to my conclusion. Can radiologists imagine making a pact with artificial intelligence without handing over control to it, and in so doing, maintain a relational or symbolic dimension in the patient approach? The idea is that this artificial intelligence more than threatens us because it has been announced for 2045 as causing a radical break that will

absolutely deprive us of all initiative. In 2045, your biological intelligence will no longer make sense. It will be overwhelmed. The solution that people like Elon Musk or Laurent Alexandre proffer is to say that you must try to jump

on the bandwagon, learn artificial intelligence, learn the information technology and mathematics required by artificial intelligence so that you aren't completely left behind in 2045 when most of

humanity will already be left on the sidelines. This idea is not completely absurd. That is what I mean by making a pact with artificial intelligence, but does that mean we should hand over the control to it and discard what is truly human? I believe that is the question the title of your conference can ask with equanimity. "Artificial intelligence, dream or nightmare for radiologists?" Thank you.

Artificial intelligence in the radiologist's world

Paul CHANG

Radiology Professor University of Chicago


F

Artificial Intelligence in the radiologist's world *

irst of all, I would like to thank the organizers for inviting me. I apologize for the somewhat abrupt transmission from the sublime to the pragmatic:

It's difficult to switch from a philosophical perspective to a more concrete one.

I will preface my remarks by explaining the context of my talk, which is on a much more concrete level. I think the misconceptions and fears about artificial intelligence stem from a misunderstanding of what is currently defined as Artificial Intelligence and its capacities, in relation to what the majority of us consider to be the usefulness. We tend to get these two phases mixed up. I think some issues that were raised are quite legitimate and deserve an answer now. But, the capacities of Artificial intelligence are only at the early stages. I’m convinced that a part of the issue, concerning the fears and uncertainties about the application * Re-transcription of the talk

of AI, is related to the future of AI rather than the current possibilities.

These are very interesting times when I believe our dehumanization, in terms of radiology, has nothing, at least partially nothing, to do with AI but rather with the fact that as relatively primary organisms, we are poorly adapted to handling the very rudimentary information systems that we have at the moment. When I travel around the world, I notice almost everywhere that radiologists limit themselves to maintaining the status quo. The workload has clearly increased and our capacity to provide our patients with good quality interpretations and all the reasonably required care is challenged by the complexity of our data sets. I think we need help.

In a moment I'm going to talk about a much shorter period, the next ten years, which is always difficult to predict. Nevertheless, I will try to base my predictions concerning AI on empirical data. The purpose of my talk is to give a practical introduction to AI.

II

Chapter II - Artificial intelligence in the radiologist's world - 27

When I talk to physicians, one of the things I notice is that many of us don't really understand what AI is, and part of the reason is that the term is misleading. It’s a bit clichéd, which leads to all sorts of false notions. Therefore, I would like to provide some general information on the current state of Artificial Intelligence, and notably deep learning, which currently constitutes the main, most attractive application of Artificial intelligence, especially in the field of radiology. I'm going to dispel some of the misconceptions by explaining what Artificial Intelligence is in reality, and if I have the time, I will talk about the practical constraints and challenges of adopting this technology.

In terms of AI there are two types of radiologists. On one hand, there are those who generally, say we absolutely need something new, because we are hardly able to do our basic work now and we can't meet the demands of clinicians and patients. I would probably have put myself in this category and I think what we're doing at the moment is insufficient and we can’t keep going in this direction for much longer.

To a certain extent, the second category of radiologists is afraid that this technology might completely replace us. In fact, many of my residents are afraid they will not find a job after their residency. I think all of this is exaggerated and I would like to clarify what AI represents today, without venturing too far into the future. What it is at present is less cause for concern and could help us to take better care of our patients. The first thing that I want to say

is that there is nothing new in all this. In the fields of radiology and medicine, in general we tend to get carried away on the subject of potentially revolutionary technologies. I heard the same predictions about PACS 1, because I’m old enough to remember and my

initial research was on digital image management. At that time, people were already saying that PACS would dehumanize radiology, etc. In some ways, we were right, but we learned to exploit this technology appropriately. We have also heard similar remarks about voice recognition, structured information

1 Picture Archiving and Communication System/Systèmes d’archivage et de transmission d’images


and big data. There is nothing new and as I was saying, in radiology we tend to get carried away too easily.

The truth is that it takes much more time, much longer than we and the prophets of doom could imagine. It takes a lot of time to completely exploit new technology. In reality, most of the times, we wait a long time before adopting them. I think it will be the same for AI, but by the time we've learned to use this technology well, we will already be behind. I predict that by the time we learn the appropriate use of AI and how to apply it most of our young radiologists will think it is about time. AI will then be ubiquitous, not just in radiology but in the real world. For the time being, we’re seeing intelligence in Alexa, Siri and self-driving cars. I predict that by the time we learn to use AI correctly, it will be welcomed with relief and will no longer be considered a threat. In general, we take a long time to adopt new technology, which is reassuring for some people. Everyone seems to be convinced that we will be the first to be affected by AI. I would argue that this won't happen like in the past, that we are becoming alarmed too quickly, and that much more time is needed to fully exploit this technology.

This hype is problematic, because we tend to get carried away too easily when in fact much more time is needed to adequately exploit the technology. That still leaves us with a lot of time to worry and that is what is happening now. Personally, I think we should stop worrying about the potentially negative impacts of this technology and, as the previous speaker said, maybe we should start to reflect on the best way to exploit it for an optimum cybernetic man-machine relationship. We have been doing it throughout the history of radiology.

It doesn't matter whether you consider AI as a golden opportunity or a threat to humanity and to radiologists in particular. I would simply say, forget about AI. We need all the help that any form of digital


technology can provide us. It could be AI or something else, I don't know. But what we’re doing now is barely adequate. That is my opinion. Our clinician colleagues' expectations and the complexity of medical imaging are such that what we’re doing now is barely adequate and soon will be inadequate. I would say, that is the real risk. It isn’t AI, but the fact that by using the computer tools available to us now, we as radiologists will soon be unable to provide adequate services for clinicians and patients. I would say, that is the real danger. So, instead of declaring that AI is a terrible threat, we should examine it closely to see whether it can help us to meet the real challenge, which consists of providing our patients with timely, good quality service. At present we’re caught between the devil and the deep blue sea. After our discussion last night, I realized that we are all facing the same challenge. We’re asked to provide services of a higher and higher quality, and interpretations that are increasingly complex but at a lesser cost. That is very difficult. I firmly believe in intellectual arbitrage and I’m convinced that in the field of medicine, we tend to lag ten years behind in terms of the appropriate use of information technology and notably AI. The other sectors tend to use computer technology to try to obtain better quality or better results while reducing their costs.

We should learn to do the same.

As I said before, the challenges are increasingly complex and so is the size of data sets. More and more we’re leaning away from a purely morphological interpretation towards physiological and quantitative imaging. The idea of generic characterization of phenotypes is no longer adequate. Our clinician colleagues want more precise information. That is the role of radiomic analysis and radiogenomics. It is no longer just theory; my clinicians are now asking for these types of analyses which are almost impossible to perform manually. I simply don't have the time. I hardly have time to measure resistance, but that isn't enough anymore. I need activity curves, perfusion data, quantitative data that are more optimized than I can obtain manually. We will need help, at least over the short term, and I believe that is where AI can be a golden opportunity rather than a threat if it is used appropriately.

We’re not limiting ourselves to sending a message in a bottle. I think that in the old days before PACS, we were much more committed to our patients and our clinician colleagues. We were less effective, because we had to go to the reading room to look at the negatives, but at least we had the opportunity to collaborate several times per day.


Now, we’re alone in our reading rooms and our reports are like messages in a bottle that we throw out to sea hoping they will be actually read. So, some people are wondering why we’re isolated and others why we’re so prone to be replaced by machines, because to a certain extent, we already behave like machines. I don't think the problem is AI. I think the problem is that as radiologists we got lost along the way. We used to be physicians and now patients and colleagues don't even see us, they only know us through the messages sent in a bottle. We should come out of isolation and go back to the old days, but through new technologies. In the past, our way of working was more human and more collaborative, but not effective enough. The question we should ask ourselves is, can we exploit these new technologies to become more human and collaborative like in the old days? I think we can find a happy man-machine medium, not to be more like machines but to be more human like we used to be.

Most of you are too young to remember the time when there were no PACS, when we communicated with clinicians instead of being all alone in our reading rooms sending messages in a bottle. We should also be more efficient and more productive and I think advanced technologies will be very important in this respect.

Unfortunately, the technologies that we use now are not fully developed. They are ten years behind in comparison to other sectors and only allow us to provide standard service.

And that is exactly why we’re barely able to maintain the status quo. I’m convinced that advanced computer tools, including AI and other approaches based on big data, will not contribute to our subsequent

dehumanization, but quite on the contrary, will help us to become more human, more collaborative.

The second misconception is that AI is new and somewhat sinister. I think that is because we don't know exactly how to explain Artificial Intelligence.


In fact, it serves many of us to present AI as something frightening. That creates a sort of asymmetry, a power struggle in terms of knowledge, an unequal division. If I know about AI, I can treat you high handedly. I’m convinced that the real threat related to AI is not presented by AI itself, but by those who control it and by their objective. The "haves" against the "have nots", that is the real threat and not AI itself. AI is like all the other tools and in a few minutes, I’ll explain why it isn’t as mysterious as it seems. But, as I was saying, at present it is in the interest of many people to make it seem as if AI is dangerous, anthropomorphous, frightening and mysterious, and that it is similar to humans and the biological systems. In reality, it is more like statistics.

AI belongs to the field of information technology known as data science, according to which information technology is used to process and extract useful information and knowledge from data. Artificial Intelligence belongs to this field. Personally, like many others who work in this field, I detest this term because of the disturbing connotation, the anthropomorphous aspect that is not useful. Many of us don’t like this term, and personally, I try to avoid it.

I prefer a definition more pragmatic and useful like "machine learning". It's a fundamental change in paradigm. This is nothing new. It has been around for decades, notably in radiology. CAD, computer-aided diagnosis, is a perfect example of machine learning. I don't know if you use CAD to interpret mammography or to detect pulmonary nodules. CAD is well-known and well-established in the field of radiology. It’s an example of Artificial Intelligence and machine learning, but we didn't feel threatened by CAD thirty years ago when we started using it.

I will try to explain the difference between machine learning and deep learning, which causes so much fear today. The change in the basic paradigm is that instead of having a programmer who creates an algorithm to determine an action, the program is driven by data. That is the fundamental difference. Machine learning for AI is contextually data-driven instead of being a priori algorithm-driven. Within machine learning we now have deep learning, which uses neural networks or deep learning to obtain similar models, without an a priori model. That is the only difference between deep learning for AI and machine learning which we have been using for decades.


At present, Artificial Intelligence follows a hierarchical model a bit like a Russian doll. What I'm trying to explain is that there is nothing new. It has been in existence for decades, from when I was a medical student many years ago. There have always been advances, but the basic model has not changed. What has changed is that it now has practical applications and this is because your children love to kill aliens in video games. That is the truth. Video games are now so popular and ubiquitous that these graphic processors have become relatively affordable on the market. It so happens that the mathematical models used to create aliens in video games are very similar to those used for crypto-currency, bitcoins, and deep learning. The fundamental difference was not a great improvement in the Artificial Intelligence paradigm, but rather the fact

that we can now use them in a practical and cost-effective way thanks to graphic acceleration.

As I mentioned before, the difference between machine learning, Artificial Intelligence and traditional programming is that instead of having an a priori algorithm, which is how PACS works with an informational model in which data such as DICOM or HL7 are integrated, algorithm development is data-driven. It is easier to explain this with pictures. I use an old algorithmic model to classify my pictures, with folders that indicate the year, month, maybe my trip to Paris, and I put my pictures there. This is an example of the traditional operating paradigm and an a priori information model: I have an algorithm and I insert my data in it. It is very effective and this is how PACS electronic medical records work.

The problem is that for deviations from the intended use it is not very efficient. For example, if my wife asks me to find a picture of our daughter at the Eiffel Tower, I have to ask for the date because I didn't create a file that corresponds with picture, daughter, etc., and the algorithm becomes useless. I don't know how many of you have used the Apple IO system. If you look at your pictures with this application, you have an immediate example of data-based classification. It uses the metadata of the picture through


image recognition and informs you that there is a picture of a little Asian girl, that there are many pictures of this little girl and creates a folder in which these pictures are kept. In other words, instead of using an a priori algorithm, the data decide how the algorithm should organize the pictures. That is the fundamental change in the artificial intelligence and machine learning paradigm. Data drive the functioning of the algorithm and not the opposite.

Big data are another form of Artificial Intelligence and it is important because this helps to understand how deep learning functions. When I teach deep learning to my mathematics or engineering students, it doesn't serve any purpose to show them pictures of neurons. That scares them because they are the ones who must actually build the systems, so, that doesn't help them. On the other hand, I teach them to use statistical methods because essentially, that is deep learning. For many radiologists there is a convergence between deep learning and artificial intelligence and another big hype term in radiology; radiomics.

They use the same statistical tools but for different purposes. We use statistical tools and data-based methods in radiomic analysis, simply so as not to limit ourselves to saying that there is a mass in the kidney and to obtain a sort of phenotype that is related to a personalized genomic presentation. We use statistical methods to gain some sort of insight, like a linear regression. Linear regression is a perfect example of a data

-based approach. There is nothing frightening about artificial intelligence and unless you’re afraid of linear regression, you shouldn't be afraid of deep learning because basically, it is the same approach.

We use elements of data, and then of mathematics and in this way, we obtain a Costa error function and we try to minimize the error between the observations and our hyperplane prediction. This is linear regression that many of us learnt in statistics in primary or secondary school. The methods are now more sophisticated, but the ideas remain the same; find models that are data-driven. We’re very familiar with all this and we use the same technologies as in radiomics to analyze and correlate


phenotype description derived from image interpretation with genomic or epigenetic molecular layout.

Machine learning uses the same statistical methods but for a different purpose. Instead of trying to gain real insight, machine learning tries to do something useful like classification. The objective is very concrete. There are many algorithms and many of us use them in the same way that many of us use statistics for radiomic analysis or other types of big data analyses. This is not surprising. It is essentially what deep learning and machine learning are. As I said before, for decades we used a certain type of machine learning. CAD for lung and breast imaging is a perfect example of machine learning where we use statistical methods, a preconceived model and obtain something useful, a classification model.

Deep learning is different from machine learning because of its "brute force". People are very surprised to hear this because they are afraid of deep learning. Deep learning is "simpler" than machine learning and will never be a threat to us. The difference is that with machine learning such as CAD, you need a more intelligent a priori. You need to have an a priori model.

For example, if you use CAD for mammography, certain more intelligent scientists who usually collaborate with radiologists will say they have a model, that cancers are ill-defined, and that there are micro-calcifications. Then, we create statistical models and train them based on these characteristics. Basically, this is a machine learning algorithm. Where deep learning comes in is when you're too lazy, too stupid or when the problem is too complex and you can't obtain an a priori model. I have no hypothesis or I'm too lazy or not intelligent enough to obtain an a priori model. However, I have tons of vetted annotated data. I have images with a priori independent confirmation that tells me that this is cancer or that this is not cancer. I replace intelligence, analysis and an a priori model by raw data. That is the key. I feed all this with a vast amount of data, and then, by simply using similar methods such as linear regression or statistical regression, nothing very sophisticated, I can obtain a curve adapted to the algorithm that enables me to obtain a classification. That is why many of us who work with deep learning would say it is incredible that we make the entire population believe that AI is something really scary. In reality, those who say that, refer to deep learning as logistic regression on steroids.


Basically, it is a form of logistic regression. What is interesting is that I can use logistic regression serially and deeper and deeper each time because now I can use a graphic processor.

Each time you attend a conference on deep learning or Artificial Intelligence, you’re shown a picture of a neuron and you’re told that something mysterious happens. First of all, it is an over-simplification of a neuron. The perceptron, which is the precursor of our modern neural network, was a data dimension reduction tool. That is exactly what the neuron does. It takes complex data and tries to simplify the results. The similarity stops there. That is why I don't like the term "neural network" because the neuron is much more complex than our primitive computational neural networks. Nevertheless, they have one thing in common, which is a statistical data dimension reduction tool. In some ways, many of us uphold the idea that from a mathematical and statistical standpoint deep learning is advanced curve fitting.

For example, it would be very complicated if I gave you a data set and asked you to segment the different random curves. It would be very difficult to use the traditional linear models. With Artificial Intelligence, with deep learning you can do really specific curve fitting. There is no mystery and this is because with the acceleration of the graphic processor we can obtain multiple regressions and connect one regression to the next and the next, and so on and so on. That is the power of deep network; being able to serially combine the result of one statistical regression with the next.

This is extraordinarily advanced curve fitting which nevertheless, creates some problems.

We will show you a slide to demonstrate that the system is incredibly intelligent, by

displaying a series of layers in which the first one detects edges, the second, noses and eyes and the next, human faces. In fact, this is quite unusual. If you look at


deep learning from a practical standpoint, as we certainly do in radiology, these hidden layers make no sense and are very difficult to interpret.

You will hear about convolution, but I won't get into that because I'm running out of time. You hear about convolution in radiology, but this is nothing new either. We're all familiar with convolutions. When you ask your technicians to put lung windows on your display screen, they apply a high convolution filter to get edge enhancement. That is what a convolution neural network is. It is using convolution filters as a model to obtain these new regressions. There is really nothing new about these technologies. Well-known statistical methods such as regression are used in a more concrete way, with graphic processor acceleration. On the other hand, what is interesting is that we can carry out many different operations. If you have a sufficient amount of vetted data, you can obtain a hyperplane or a predictive model.

These systems raise a triple challenge. The first is how to validate systems. I really like the term deep learning, because the word deep has two connotations which I find apt. First of all, it means "thorough", and these systems are incredibly efficient, as you know.

But, it also means mysterious and I really like that connotation. It's not easy to understand how they work. This is unlike machine learning such as CAD where you can have an a priori model and be transparent. We think that cancers are ill-defined. Therefore our statistical model finds ill-defined borders. This is very reassuring for us, because this seems very rational. The problem with deep learning is that it functions but I can't explain why because I don't know. I was too lazy to obtain an a priori model. I just fed in a large amount of data and it found a solution. This creates an entire series of problems, because now the hidden layers are not obvious representations and therefore, the validation must be purely statistical, which means that, not only do we need a large amount of data to train these systems, but also a large amount of data to validate them. This raises the question of whether in medicine we have this enormous quantity of verified data, and the answer is no.

Those of you who are afraid of AI can rest assured because we can't even train these systems. That is the reason why the examples of use that you see are relatively elementary, because they’re based on available data rather than real examples of use. This indicates a lack of development in these systems.


For those of you who, like me, want to use these systems, this is bad news because we won't be able to count on AI in a hurry because of the unavailability of an adequate amount of data.

The second problem is overfitting. The best image recognition system in the world based on AI mistook a turtle for a gun. I won't get into why. This was used as an example by MIT deep learning students in the United States to demonstrate the problem of overfitting. This is unlike standard machine learning where there is an a priori model that can be presented and where you can expressly state that you think the cancers are ill-defined, which is why we used our statistical model. Deep learning means: "I was too lazy and too stupid". I had a large amount of data and they fit the curve

because we do not know for me, but you do not know what it fit. Is it overfitting? Regardless of how it works, the system thinks it's a gun. We have the same data set with two hyperplane predictions, one is linear but the other is lots of air. Which of these predictions will you take into account to generalize to the other data sets? This is the primary problem with deep learning at present:

if you don’t have a very large amount of validation data, how can you know whether it is overfitting?

I do a lot of research for Philips. Several years ago, I participated in a research project on a simple deep learning experiment to find out whether a deep learning system could be trained to detect a part of the body on an X-ray. It is not very useful in practical terms, but it was amusing to see. We traced the traditional learning curve and it functioned very well, with an accuracy of about 99%. Unfortunately, the system thought that it was a mammogram. Each time that a part of the body appeared on the edge of the X-ray, the system knew that it was a mammogram. This is actually very clever. Most of the time, the technologies that we apply to another part of our anatomy tries to cut the


anatomy on the image. Unfortunately, we cannot ask our patients to move their breasts so that they appear on the center of the image. Mammography is relatively unique because the part of the body that is concerned is generally placed asymmetrically in the image. Therefore, the deep learning system used this notion, which is not wrong. In fact, it is extraordinarily clever, but can this be used as a clinical criterion? That is the problem with deep learning. It is deep, it is obscure. You don't know exactly how it functions. Unlike standard machine learning, which uses an a priori model, you don't know exactly what is happening.

Therefore, we must have an adequate volume of metadata and integrate work flows. That is the essence of deep learning. It requires a very efficient IT infrastructure and, unfortunately, we don't have that yet.

I would say, very quickly, that we’re ten years behind in comparison to other sectors, in terms of IT infrastructures capable of supplying these systems. Those of you who, like me, want to use AI must find mechanisms capable of creating the data required to supply these systems, train and validate them, mechanisms capable of validating our IT infrastructures and integrating them into our work flows. That is a problem. In your notes, you have the

architecture we use at the University of Chicago and you can send it to your IT manager.

In conclusion, it is probably too early to choose a winning strategy for AI, but you can start by providing the necessary data or the required infrastructure.

I will stop here and give you a few examples of what you can do with deep learning now that it can help us in a practical way. For example, when you look at MR, many of us know that to be efficient we must look at the money sequence. For brain imaging it is the FLAIR sequence; for abdominal imaging, I look at the diffusion weighted image (DWI) or T2, and after that we listen to Where is Waldo.

Now, we must look for the lesion everywhere, etc., and this makes us waste a lot of time. With machine learning, I find the lesion and automatically identify all the other sequences. I do the subtraction automatically, which makes me save time with the acquisition. I check to see whether or not the results are better and that helps me to formulate a better differential diagnosis. This will not replace me, but it will enable me to spend less time on the logical and technical work, spend more time on collaborative work and on


relationships with patients. This will not replace me, but it will optimize the man-machine collaborative relationship, like when I wear glasses. Personally, I no longer do divisions with several figures manually. And yet, I don't feel belittled, quite the contrary. It is the same thing with machine learning.

In the United States, we have a big problem with accidental discoveries, nodules that slip through the cracks and that are not monitored in any way. In Chicago, we’re actually working on this problem at the moment, thanks to deep learning. I identify the nodule and the deep learning system immediately understands that it can help out its human colleague. It can verify whether there is a risk factor, a history of smoking, automatically verify the latest Flector recommendations and populate a database to enable monitoring. This does not replace radiologists, but relieves them of busywork so that they can spend

more time with their patients.

Is deep learning a threat for clinicians? If you look at the other sectors, you will notice that the best AI-powered systems function better with human involvement. The systems are not aimed at replacing men or women, but at improving their services. Is deep learning a threat for clinicians? No need to panic. We have fed this type of fear many times in the past.

This is nothing new. Radiology has always been capable of redefining itself positively to incorporate new potentially disruptive technologies,

often in the interest of our patients. We have always taken time to adopt new technologies, even though "we do quite a song and dance" in the beginning. Deep learning is neither an awful threat nor an absolute magic formula. If we learn to master it, it will neither be more nor less than the other technologies that we have learned to use.


In conclusion, it is probably too early to choose a winning strategy in this field, but you can start by providing the data necessary for the required infrastructure. Most of us are woefully unprepared to use AI. We must find examples of use and not let ourselves be guided by the available data but by useful things. We must strive to integrate AI in the work flow. The goal is to obtain optimized proficiency of man-machine cybernetic work flow without replacing radiologists, but by enabling them to improve in such a way that all the technical work is done by the machine while we spend time on the collaborative and human work and on analysis, poetry and philosophy. Let machines to do the work that we don't know how to do well and should not do. My final recommendation is to remain motivated. Ensure that radiology is organized and let us hope that they can assume a position of leadership so that this technology is used correctly.

Thank you very much. n


Artificial intelligence

in medical imaging: hope or Pandora's Box?

Luc SOLER

President - Visible Patient, Research Director - IRCAD


T

II

Artificial intelligence in medical imaging: hope or Pandora's Box? *

hanks to the organizers for inviting me to give this talk on artificial intelligence in medical

imaging. A very long time ago, fire was stolen from a certain Zeus by a certain Prometheus who gave this fire to Humans. I don't know if you know this story from Greek mythology. Zeus was really not happy with this theft. So, he punished our dear Prometheus who gave us Humans this fire, by hanging him on a rock where an eagle went and ate his liver every day. The eagle was not stupid, he didn't eat all Prometheus' liver and this allowed it to grow back. He already understood at the time that you cannot cut off more than 70% of the hepatic volume if you want to have effective liver regrowth. So, the liver grew back. It was a perpetual punishment.

Why did Zeus do that? Because that fire that was stolen from him, gave Humans the possibility to evolve. But maybe you didn't know that Zeus added a second punishment: * Re-transcription of the talk

the creation of a certain Pandora, a magnificent woman created by six divinities. Among her primary characteristics, such as beauty and cleverness, Zeus asked Hermes to add an ugly little defect: curiosity. Then Zeus gave Pandora a pretty gift, an amphora filled with all the evil on earth that could destroy humanity. The famous Pandora’s Box was in fact, an amphora because as you may know, at that time boxes were not really their thing. Zeus explained to Pandora that she should not open this amphora without telling him why, but Pandora's curiosity got the better of her and she ended up opening the box and allowing all the evil on earth to escape, and that is why today, we are ill, we become ill and we die. In fact, Pandora closed this box before the last evil could escape. And this last evil was called "Elpis", which was incorrectly translated to mean hope. The right translation of “Elpis” in Greek is to wait for something. The opposite of this word means to "act" and it is quite understandable that if we act instead of waiting and doing nothing, maybe we can change things, and notably combat evils such as diseases.

Chapter II - Artificial intelligence in medical imaging: hope or Pandora Box? - 45

On the contrary, if we wait without acting, for example, for a car that is coming towards us, there is a very high risk of being run over. Since this last evil did not leave Pandora’s Box, humans were able to act and thereby use the fire that Prometheus gave us, notably to act on the liver which grows back. So, we used fire to cauterize then to do thermoablations inside patients. But in order to do this, we needed other sources of energy and the use of our human intelligence to act. In the case of thermoablation, developing and controlling fire was not enough because one problem remained: aiming for the target which is inside the body of the patient who is not transparent.

To aim for the target, we created medical imaging devices which enable us to see this target inside the body. We can also use this imaging device otherwise to perform radiation therapy. We used it to both aim and acquire x-rays that enabled us to destroy tumors. It can also be used in surgery. In this case, the idea is to preserve tissue. To simplify, we can compare the liver with a tree, the portal venous network corresponds to the branches of this tree. When there is a sick tree in a town, the town council usually decides to cut in down and replace it. In surgery, this is called transplantation. If it is a tree in a home, the tendency is to try to save it and to only cut the sick branches. But in order to do this it must also be possible to see where these

branches are and where they start from.

For the liver, the surgeon can only see the base, that means the trunk and the origin of the first branches and he must cut the branch that goes to the right place. Here again, to find our bearings, we use medical imaging. It is quite clear that medical imaging is crucial for decision-making and treatment, but this imaging causes a few problems because it is complex. Moreover, the disease is sometimes only on very few sections as is the case in this example where the tumor is only visible on 3 sections out of 237.

There is a second problem: How do we find our bearings in a real patient using these images? If we look at our clinical routine, based on this image that is initially in three dimensions, the radiological report will tell us, "The tumor is located at the end of the right paramedian vein in segment eight." It is exactly as if I said to you, "meet me here tomorrow at the end of Avenue du Général Leclerc in the fourteenth Arrondissement in Paris", and I only give you this satellite picture to get there. You see that it won't be easy. And yet, this is the routine for all clinicians. A 3D image is used but it is complex to interpret. It is translated into text that provides additional information for pinpointing, but in the end all you have is


a satellite picture and you have to perform an operation on the patient. Every year, this type of complexity results in 40,000 deaths in the United States due to errors in diagnosis or the choice of therapeutics in surgery. These errors can be avoided if the way that the image is perceived changes. In fact, what we had was an image that was actually in 3D but cut up into slices. However, due to its limits, our brain cannot effectively and systematically interpret and reconstruct 3D information contained in the image correctly. But this human limit can be surpassed if data processing assistance systems are added, that are based on artificial intelligence, for example. As the previous speaker rightly explained, the goal of artificial intelligence is not to replace Humans but to enhance them. And they can be enhanced by many means. Robots and instruments that enable us to be more precise can be added to increase our manual capacity. Remember that this is what Humans have been doing since the dawn of time. We were unable to cut with our small fingers. So, we created scissors and sharp tools. Creating these tools enabled us to advance.

Controlling fire enabled us to advance. We can also enhance our visual perception. Creating an image that enables us to see better is, in a way, the objective of radiology, especially if in addition, our cognitive capacities, which are still also limited, are enhanced. We have a limited memory. Why not use that of the computer?

In order to do this, the initial satellite picture is transformed into a map. That means that in the picture, the contours of structures of interest will be detected and named.

This map that can be made for a town can be made for the patient. This is what is developed with Visible patient. First, the medical images are sent via the Web to secured storage systems.

Securing the data is very important because we’re talking about a patient. It is not about my banker saying to me, "ah, but according to artificial intelligence, in ten years you'll have cancer, so, I won't give you a loan for twenty years. But, if you want, I'll give it to you for five years." That is not the goal.


The goal is to take care of the patient. Therefore, the data must be secured. This secured data can be processed for the good of the patient to produce a 3D model of the patient, a virtual copy, a digital clone. This is the analysis that Visible patient now offers. Like a blood testing laboratory that provides a report, Visible patient provides you with a 3D model of the patient, a virtual copy that will enable you to plan the surgical procedure.

In concrete terms, this is how the surgical procedure is planned. Here you see the same patient who I showed you earlier. Here is a tumor at the top of the liver, here. It is not necessarily easy to see. There is a second one here, which was not identified on the radiological exam or by the surgical team, but which was detected by the software. When I show these two tumors to hepatic or digestive surgeons, regardless of whether they are experts or among the youngest, they all conclude that they are located in the right liver. Remember that we train more than 6,000 surgeons per year at the Strasbourg IRCAD and I have been showing this case since October 2017 in the courses that we teach. More than 500 surgeons have voted on this case. Well, every single one of them was mistaken, including the top experts, about what should be done for this patient, about the choice of treatment.

They all say the right liver should be removed.

Let's see what we can do with a 3D model. So, here is the same 3D patient. First of all, it's much easier for any man in the street to understand because here, there are reference points that anyone can recognize. For example, the ribs, they are an anatomical reference point that is quite easy to identify in the body. Like a GPS that displays the list of streets, the list of organs can be displayed here. Next, transparencies can be used for any anatomical or disease structure that has been modelled individually. Here, I'm putting the liver in transparency. If I erase the bones, the two tumors in this patient are quite visible. If we use standard anatomical reference points such as this branch which is the middle hepatic vein, you will say, "Yes, actually the two tumors are clearly on the right". But, I used a false anatomical reference point. However, this is often taught in anatomy and in our departments. This anatomical reference point based on the position


of the gall bladder and the middle hepatic vein is false because it is not the hepatic veins that determine the functional definition of the liver, but the portal veins, those in purple. Luckily, with 3D modelling, Visible Patient also makes it possible to simulate the placement of surgical clips. If surgery is simulated wherein a clip is placed on the right portal branch, we then immediately see the real right liver, virtually de-vascularized. The software also provides us with the clamped volume and the remaining volume. Here, in transparency, you can see the right liver in orange and I notice that the two tumors are not at all in this right liver. In fact, by putting a clip in the left portal branch, you see that finally, these two tumors are in the left liver. By the way, you will see that the separation between the right and the left liver is horizontal and not vertical. This is not obvious for a normal human brain, even that of an expert, to understand a difference such as this, which seems to be abnormal. The truth is that this difference is not abnormal because in 1957, Couinaud had already referenced it and indicated that it was present in 16% of patients. In general, all the published studies show that when a hepatic surgeon is asked to give a layout of his surgery using medical images and the radiological report, and he is then given the 3D model with the possibility to simulate that Visible Patient provides, in one third of the cases he will change his surgical plan to make it more suitable for the patient.

This makes it much easier to understand mortality due to diagnostic errors and errors in the choice of treatments because, in fact, each patient is different. They all have an anatomy of their own and the medical image analyzed in 3D makes it possible to reconstruct and understand and this is not only for the liver.

Here is an example of pulmonary atresia. For this operation which was in a live surgery course, the surgery was described before it was performed. "The disease is in the right upper lobe, so, I am going to remove the right upper lobe". In fact, when you look at the sagittal view in particular, it seems to be clearly located in the right upper lobe. While, as was the case with the liver, look at this six-month-old child in 3D. We can virtually remove the skin, then remove the bone structures so that we can see the lungs better. The right lung is put in transparency then with a right click of the mouse on the right upper lobe bronchus of this lung, I perform a virtual clamping. So, you then see here in yellow, the right upper lobe that the surgeon wanted to remove. Now, here is the diseased part in 3D and notice immediately that it is not in the right upper lobe at all. Without the software, you would remove the wrong part. That means that a serious avoidable error would have been committed. An avoidable error is understandable. A serious error means that the patient must at least undergo a repeat surgery to correct the error. There are 92,000 per year in France.


These are the 2012 figures from the Ministry. Now, this can be avoided thanks to the software.

Quickly, here is the final example, in urology this time. A-five year-old child has two tumors, one nephroblastoma in each kidney. There are only five cases per year in France and it is understandable that with such a frequency it is difficult. You can see on the medical image that the left tumor invades almost all the left kidney and that the right tumor invades almost half of the right kidney. The therapeutic choice validated by two distinct clinical teams is the following: remove half of the right kidney and remove the entire left kidney. During the six postoperative months the child would be placed on dialysis due to the renal failure expected from this surgery, then, if there is no tumor regrowth on the right, a transplantation would be performed as soon as a transplant is available. After that, it would be a life of anti-rejection medicine. Except that with the 3D construction produced by Visible patient, we can see that the tumors are not as invasive as anticipated. Therefore, we will simulate the surgery again preoperatively. Here, we erase the renal vein so that we can see

the arteries better and put a clip on this artery that supplies the back of the tumor and this one that supplies the front. As you see from this simulation on the right kidney, I remove 60% of the right kidney here, but 50% of functional kidney remains with a certain volume. That is what is most important. On the left kidney, we can see that there is a small anatomical variation hidden behind the left renal vein that I can erase virtually. We have two renal arteries. We will sacrifice the one above by placing the first virtual clip. Here, we will also sacrifice a second small artery to ensure

complete removal with a safety margin that was also modelled in 3D. After this operation, note that only 17% of the volume of the left kidney remains, but in fact, one third of

the volume is functional. Because both kidneys are larger than average, the decision was made to attempt this surgery without added risk for the patient. The first surgery was performed at the beginning of December 2017 and consisted of removing half of the right kidney. At the end of December, the patient had no renal failure and therefore no dialysis, which therefore confirmed the software's assessments. So, at the beginning of January the enlarged upper part of the left kidney was removed. The child returned home at the end


of January, without renal failure and without dialysis. Since then, he has been doing very well and now this is what his kidneys, that were modelled postoperatively, look like. He has two pieces of functional kidneys, no dialysis and has had no transplantation.

These are only three quick examples. Obviously, I could show you many more but let us stop with these three. I would simply like to point out that this modelling has another advantage: It enables us to explain the disease and our suggestions for treatment better to the patient. We’re often told, "AI will destroy humanity or human relationships". You see, it is exactly the opposite because in this case, thanks to AI, we’ve been able to create new relationships between the patient and the doctor who, prior to this, did not know how to explain the disease and especially the treatment, to the patient. As you can see, with the image at the gray level, it is complicated while with the image in color it is simple. There is a colored part that we will remove. We will keep the other one. We already have the figures. It is a lot simpler to explain and therefore, we improve the human relationship.

Afterwards, it can be used during surgery, either on a tablet or by literally connecting it to an enhanced reality system. In that case we will guide the surgeon's actions in the way that a GPS works. In the future, thanks to these technologies, we will see patients in transparency. In this example, by using the Microsoft Hololens headset connected to the enhanced reality

software, I see the patient in transparency. It can be done for percutaneous treatment, but it can also be done in laparoscopy. We can connect it to a robot, for example. With the robot, I will have the same 3D view in transparency. Here we can see the liver in transparency which makes it possible to operate tomorrow while being guided by the image, the virtual action that we would have planned ourselves before the surgery. That means the preoperative simulation serves as a guide, like the GPS when it shows the road that you will follow.

So, obviously you might think, "But, in that case, why not use the self-driving car after all?" To do that, it must be connected to a robot that I can control. But more precision is also needed, such taking into account deformations and breathing movements in real time. At present, with the software developed at the IRCAD and the Strasbourg IHU we obtain a precision within two millimeters. By connecting it to a robot, we can also follow the breathing movements and ask the robot to filter them. That is what we do with the AVR team at the University of Strasbourg. Here, you can see that without filtering, the tumor on the liver moves because of the breathing movements. Obviously, he could put the video on pause to stop the movements, but the surgeon wouldn't like that. A second solution would be to stop the patient from breathing. In that case it is usually the family that is unhappy after the surgery.


The third solution is to use what I showed you before. That is, enhanced reality linked to a robot with artificial intelligence. Real-time filtering of the movements is obtained with the robot following the movements of the tumor very precisely. So, is it artificial intelligence? Not really. This is applied mathematics on a computer that controls a system. Nevertheless, in general, it has been told what it is required to do. After that, it found solutions. In fact, it's rather deep learning, but in the end, we have something interesting. This helps us because the tumor remains at the center of the image and the instruments move at the completely automatic rhythm of the breathing. So, I remove the difficulties.

This artificial intelligence scares people at present. It makes people afraid because they think, "But in the future robots will replace us. If they replace us, maybe they will one day say, maybe we don't need humans". If we return to the

beginning of my talk, some people think that developing AI is like opening a Pandora’s Box. Elpis, the last evil in the Pandora’s Box is waiting for something. If in the future, I ask the computer to manage the computer, if I say that finally, everything is in its hands, then certainly

we will find ourselves faced with Elpis, which means doing nothing and sitting in front of our computer which thinks and acts in our place. But if artificial intelligence is placed in the hands of humans, in that case it will be like finding ourselves with the fire that Prometheus gave us. We learned to domesticate this fire and this enabled us to evolve. There must be some pyromaniacs, so there will be risks in using AI, but we can certainly domesticate it.

The fundamental question is, should we deprive ourselves of AI on the pretext that it can be dangerous? I will answer you frankly. I cannot agree for us to deprive ourselves of it because every day we have patients who die of diseases that we want to treat. These children have the right to be able to embrace life to the fullest. In conclusion, I will say one thing. To live is to take risks. Intelligence is to control them for the good of humanity. Thank you for your attention. n

Ethics in the development of

Artificial Intelligence

David GRUSON

Sciences Po Paris Health Chair Ethik-IA


T

II

Ethics in the development of Artificial Intelligence *

hanks everyone. Since a part of our works consists of considering the interactions between artificial intelligence and time, I will try to catch up so

that we can finish on time. Especially since some things were acknowledged in the previous speeches. It has been clearly identified that radiology is an advanced point in the development of artificial intelligence in the health field. What we do with the ETHIK-IA initiative, a civic academic initiative that includes academics, the health chair of Sciences Po Paris and the Paris Descartes Legal Health Institute. They are both medical lawyers and sociologists. I would like to welcome Doctor Victor de Castro who is about to stand. He is the medical coordinator of the initiative and later on Florent Parmentier will talk to you about geopolitics.

One of our goals is to try to show that, precisely as the previous speaker suggested; we can develop regulation

tools to try to control the risks related to technological development and robotization, and capitalize on the gains with a basic conviction. If you think about it, there are ethical issues at present that are related insufficient use of digital data in our health care system. These ethical issues are acknowledged and are quite tangible. You cited the example of chronic renal failure. This is quite a striking example of one of the unacceptable problems in terms of the quality and effectiveness of management as a result of insufficient guidance by health data. These ethical issues are infinitely greater than the potential ethical risks related to the deployment of technologies. These are potential risks. We can try to regulate them and anticipate them. Claude Kirchner, the president of the INRIA 1, and I coordinate the National Council of Ethics which is preparing the revision of the bioethics law, in which Jacques Lucas is kind enough to participate also. It is not in that capacity that I am speaking this morning. It is from a personal standpoint.

* Re-transcription of the talk 1 Institut national de recherche en informatique et en automatique (French institute of computer and automation research)

Chapter II - Ethics in the development of artificial intelligence - 55

Many things have been said about big data, some are myths and some are facts. There is an apparent operational clarity: the massive collection of data. This is a link you understand well through connected objects. There is an apparent legal clarity. It has been one week since the enforcement of the general data protection regulation (GDPR). We have a sophisticated personal data protection framework. Standards are being deployed. There is an apparent clarity in terms of public health. Massive collection of data should enable us to advance public health and knowledge.

In reality, you all measure it daily. Big data is a more complex reality. It is a bit like Maastricht in the Belle Époque. It's a non-neutral notion. There were opponents in Maastricht who said "Maastricht" with exasperation, and there were those who were in agreement who said "Maastricht" with a big

smile. Big data is a bit similar. There are those who are in favor and those who are against. It is a notion that is not neutral in the public debate. It should actually be assessed. Behind this complexity, there is an ethical issue which is that big data seems to be hardly operational in the field of public health because it might give the impression that all health data are equivalent.

You weigh it up well in your professional practice. All health data are not equivalent. Without a doubt, 90 to 95% of the data should be shared and distributed better. That is the case with my mobile. No doubt this is the case for elements of medical reports that help to improve health. Moreover, it is without jeopardizing any subsequent announcement, which is one of your goals. That is all very well and good. However, concerning the remaining 5 or 10%, consideration should be given to the additional level of protection for certain data. This concerns the complete genome, for example. I'll go back to this in a moment in relation to what we do for Imagine.

The challenges of regulation have been hashed and rehashed by science fiction which often portrays a very disturbing picture of these technologies as potentially being aggressive for the human body and our health. What will become of our bodies in artificial intelligence robotization mode? I won't go over that again. That was perfectly done by Professor Chang who


really went back to the mathematical roots of artificial intelligence.

You have a picture. Don't worry, it isn't an explosion of cauliflower. It's a blob. I don't know if you know what is a blob. It is a cellular organism. It is a cell, but the largest cell in the world with a capacity to develop, learn, and along the way, learn from the consequences. It functions kind of like a machine learning algorithm. It has the capacity to absorb the other blobs in its path. A blob that has learned and that absorbs another blob that has not learned, transmits its capacity to learn to the absorbed part. Take a look at the research of Audrey Dussutour from the University of Toulouse on these questions.

To present the fundamentals, in addition to what has already been said, I would just like to mention in passing, the idea of genetic algorithmics. As Professor Chang reminded us, none of these notions are new. The concepts of genetic algorithmics were formed at the end of the 1980s by John Holland and David Goldberg. When the principles of machine

learning were formulated, these computer researchers discovered a phenomena, algorithm machine learning which is capable of self-correction and producing new versions of itself over time. At the same time, they did not know how to encapsulate it. So,

they metaphorically borrowed concepts from genetics. The end of a code that changes is a mutation. A code 0.1 is a gene. They imported the concepts of genetics without knowing that thirty years later, today, machine learning could be applied to genetics itself.

The fact that machines and even robots influence the future of our bodies – I am speaking more as a lawyer – is not really a novelty. There is an old and increasing influence of these technologies in biology, pharmacy, surgery and even psychiatry. Maybe you are familiar with the Eliza experiment which today would probably not be considered politically correct. The experiment was conducted in the beginning of the 1960s by Professor Weizenbaum. He took a group of depressive subjects. He divided the group into two: group A and group B. He made them interact with a computer and with a psychotherapist. Group A exchanges


written messages with a psychotherapist who is really on the other side of the screen. Group B talks with Eliza, which is the first chatbot in history. It was the early 1960s version of the chatbot. It says, "That's interesting. Can you tell me more? Can you rephrase your question? I am happy to listen to you. Can you say exactly what you're thinking?" At the end, Weizenbaum implements a satisfaction questionnaire. Did this help you? The results were the same for Group A and Group B. This is certainly a lesson in humility for all of you.

The idea that all this might be new even within our strongest and deepest psyche should obviously be resisted. Similarly, from a legal standpoint, the idea that there will be a drastic change concerning liability or the rules of liability should be checked. At present, 99.5% of the subject can be understood through the standard principles of liability for damage caused by inanimate objects and also by our law of May 1998 on the liability for defective products in which a1985 Community directive was adapted. 99.5% of the subject is covered, but the problem is the remaining 0.5%. That 0.5% is the share of damage that will occur with the development of machine learning and especially the development of all the applications that were presented in the previous talks.

One of the limits of the law of May 1998 is that the designer of the computer solution,

the designer of the defective product cannot be held liable in the event of a development risk. The development risk is the risk that couldn't be anticipated considering the scientific knowledge available when the product was marketed. So, it's obvious that if the exemption of this producer is maintained within the scope of machine learning, it would mean that the damage is never indemnified because structurally, algorithm as it evolves over time does not correspond with a pattern that can be anticipated according to the available knowledge at the time it is marketed.

Here is a subject with a level of exposure that is certainly higher in the field of imaging and which obviously should be considered from this standpoint in relation to the Turing test which you are quite familiar with. In the original version of the Turing test, Jean-Philippe Masson is the human being. I will pretend to be the robot and then you have a third party human observer who is Professor Chang who is responsible for looking at our written messages. Professor Chang must know who is human and who is machine. What Turing says in the original version is that, because you are a human observer, you will be able to detect inconsistencies in human language. Do you see the share of psychology that is added, the share of imprecisions? Therefore, I will be more skillful as a human in detecting the language of my fellow humans. The problem is that in the image recognition


solutions that have been presented up until now, AI will have to decrypt a raw material that is already in digital code. So, you see, the Turing test is reversed. This is a pattern where in principle AI is more skillful because it is in its own language field. Therefore, in general, the presumption of imprecision is rather based on the human observer than on the machine. Obviously, all of this is a priori and the a priori is the radiologist's expertise. I am convinced that it will end up being maintained and evolving in these forms. The fact that you are examining the question is encouraging.

Finally, the question is less about the future of the human body than about the risk of marginalization. It concerns the development of connected objects which challenges the person/product barrier, the alienation effect of the notion of a legal personality and the body and then what we do ourselves. In other words, we spend our time improving our digital personae with all the data that we provide throughout the day on our favorite social networks and all our applications. We ourselves participate in this ethical risk-taking movement.

There are fundamental questions that affect our future as a human society. Unlike what science fiction tells us, the risk is not the domination of Humans by machines because machines are controlled by their initial

program. It can be assumed that a minimally responsible programmer who wants to penetrate the market will abide by Asimov's first law; the one that says that robots cannot harm human beings. Except that when Asimov wrote his book he couldn't imagine artificial intelligence which is a collective processing tool.

To achieve the goals of its program, AI could actually take risks for a person individually in order to achieve our collective happiness. This is undoubtedly the main risk. It is a principle and a typical pressure in the field of public health. Individual damage is created to generate collective well-being. AI takes this to a technological level, undoubtedly never known before.

A small sequence of product placement is the subject of a book that came out 48 hours ago that addresses the question, "what would happen if we were to let artificial intelligence handle an epidemic like Ebola? How would it behave? How would the administrative and political machine behave or act ex post facto? It's a book that takes a similar stand to Paul Chang. The subject is not technology itself. The risk is the way in which we use it and the way in which individual behavior is positioned in relation to this AI. What is funny is that since the book was published it has become a computer


object. I mean, it was released on social networks. It is quite a sensation. So, because it is a sensation, Amazon AI has anticipated the release of the book on Amazon. AI is putting in the plug for a book on ethical regulations for AI. We'll try to keep up with this.

The second question is what I call the Netflix effect. Let's do a little survey. Who in this room has Netflix? I'm quite a consumer of television series. Since I've had Netflix, which is five months now, my consumer satisfaction has suddenly increased. I can't give you an example of series that Netflix has suggested that I refused because it knows me. It's getting to know me better and better. Moreover, every time I receive a suggestion, the recommendations are 98 to 99% and my satisfaction increases and the quality of the product as well. That is how I went from "Les Cordiers, Juge et Flic" (The Cordiers, Judge and Cop) to "La Casa de Papel" (Money Heist). You see, there is a qualitative improvement. In the same vein, I no longer read Télérama. I talk less with my friends. The depth of my critical judgement has undoubtedly declined a bit. At any moment, I can refuse, say no, but in practice consent has been delegated to artificial intelligence. The same phenomenon exists in algorithmic medicine. It's the same risk. It is about the capacity of the patient and the primary care physician to take a step back in terms of a treatment that is recommended 98 or 99% by AI. It is not a problem in itself.

The question is simply the techniques to be implemented so that we can be objective about this.

These are common ethical points of conflict. In terms of ETHIK-IA, we're convinced that in order to try to address these issues, we must try to put as little as possible into hard law because if we put too much into our national hard law, we will partially miss out on an opportunity. We might block innovation. If we take excessive precautions with respect to the regulations in France, the risk (second Netflix effect) is that technically and medically effective AI is designed elsewhere in an unregulated setting and that in more than two or three years patients and professionals ask us to import it. The subject is not just about being protected only in a national framework. It is about the fact that if we protect ourselves too much here, we will certainly be faced with the importation of unethical AI in a few years.


How can we move forward? I will end on that note. In two minutes I will have almost caught up. Five keys were put on the table. It is a small tool that was distributed three months ago, and we asked for comments on it. For the moment, the reactions are very positive. That is good, but it is supposed to encourage debate. These are five keys for AI regulation and health care robotization and with each an attempt is made to target the minimum legal standard. The first key is patient information and consent. To be honest, everything is already in the law on the principle of consent. What should surely be added, is the requirement to inform the patient beforehand in the event that algorithm is used in their treatment, it is not provided for as such. Methods should also be included. But this is not within the law. It has more to do with defining practical methods, adapting how consent is obtained. An example would be to increase flexibility by having a more sequential capacity to obtain consent whereby the patient's agreement on several possible options is obtained from the beginning of care, ab initio. These would be options

that AI may or may not choose during the process.

The second principle, which is newer, is the human guarantee to read. You understand very intuitively. It is the idea that you keep a certain dose of human guidance in the process. That is quite in keeping with the principle. It is certainly

even better in practice with concrete techniques. Or there could be professional certification with a standard we're working on. For example, with a standard considered as a human guarantee for AI, we could say it is a good practice if every two or three

months, 30 or 40 medical records are identified in a targeted random manner, ex post, without prior authorization, so that a second medical body, to which patients would be associated if necessary, could give their opinion to ensure that AI and Machine Learning remain on a tenable ethical path. This would certainly help to answer a part of the questioning that Professor Chang asked. In principle, the black box effect of deep learning should not be controlled, but rather monitored as you go along to ensure that


it remains on a tenable development path.

The second principle that was presented yesterday in the E-Health World congress with the French Telemedicine Association is the telemedicine of human guarantee. This is directly applicable to radiology. I'm a primary care physician. My favorite image processing algorithm suggests a solution. I'm not very sure. I call Doctor Masson. I need a second expert opinion, either in an area of specialization that doesn’t have enough data to enable a reliable diagnosis, or I need Doctor House, the old hand who is experienced and will be able to tell that things are neither arithmetic nor mathematical.

The third principle is classifying regulation according to the level of data sensitivity. The GDPR is an advancement, but the protection is undoubtedly too formal and too strict in an entire series of segments. That is how it is. There are certainly elements that require improvement. On the other hand, it does not protect against the real risk which is the risk that sensitive data might be collected by artificial intelligence that we would enter into an information system, but for which we would not control the production bases. This is what we're working on with the Necker IHU 2 Imagine. These are a few good practices for the application of AI applied to whole genome data,

2Institut Hospitalo-Universitaire (University Hospital Institute)

with, for example, a distinction between genomic data warehouses and phenotypic data warehouses. In a base with a more protected sensitivity boundary, this would make it possible to conduct more extensive machine learning experiments on whole genomes, but with an adequate degree of assurance that these genomic bases would not be tapped elsewhere.

With regards to support for adapting professions, I won't go into the details. Obviously, it is fundamental for your specialization. I will restate my conviction. I don't believe you'll become extinct, if you'll pardon the term. I think that the fact that you are examining this question is a positive sign. The comment I make about imaging is also valid for the different professions to the extent that an entire series of variables are unknown at present. For example, if AI is developed, and we can count on it, in a given field, what will be the respective places of the medical profession and the paramedical profession? Several patterns of evolution, respective re-positioning or even the disappearance of this or that specialization are possible. Why would it be the medical specialization that disappears and not the paramedical specialization? Finally, why wouldn't it rather be what is our primary element of analysis in the health care field?


In two or three more years, we believe the human resources (HR) impacts will first affect the specializations or professions that do not concern actual patient care, the entire logistic, administrative and technical back office. Why? The reason is that there are already AI solutions in other sectors of economic life that are functional in relation to all aspects of HR management, both administrative and accounting.

The last principle to supervise the previous four is independent external supervision. There are several plans. In this case, political arbitration would certainly have to be restored in bioethics law. Personally, I think purely private supervision is sufficient in an innovation development plan, ISO 3 principles, certification principles to which you are quite accustomed in radiology. The thing is that in this national political atmosphere, I'm not sure whether this purely private plan can get across to the audience. Of course, we shouldn't try not to suggest a purely public plan because then it would be too restrictive. Innovation would be blocked. Maybe an intermediate formula would work. Moreover, the National Board of Medicine somewhat suggested something like this, a superior regulation authority invested with a certain degree of power and sovereignty, and then below, soft law, regulation mechanisms and certification principles like those I mentioned a while ago.

3 International Organization for Standardization.

In conclusion, it is obvious that in the history of our data protection act since 1978, France became regulated but has a lot more difficulty identifying and supporting development launchers. You are in advance in exposing AI development with image recognition solutions that are already functional. You are aware that there is an impact on the professions and that it must be anticipated. That is what you're doing. That is very good. It comes at the right moment because by chance or by transcendence, France will have to give an opinion on this question in the bioethics law of 2018. It is an important legislative date. Too much cannot be put into the law, no over-regulation, but certain basic principles shouldn't be missed. It is the right time to do it because if it is not done here, the main ethical risk is that unethical solutions designed elsewhere in settings that are less in conformance with the principles of an advanced democratic society will be imported. Time is running out. Thank you for your attention. n


Jacques LUCAS

Vice President - CNOM

Artificial Intelligence and

medicine: the official recommendations of the

medical board


T

II A. I. and medicine:

the official recommendations of the Medical Board *

hank you for inviting the Medical Board to speak on this subject that is of concern to you. Our cooperation with the FNMR, the French Radiologists Union and the

French Radiology bodies in general in G4 1 is long-standing because we already produced a guide on good professional and ethical use of teleradiology in 2005. Then, the subject became monopolized by organizations with somewhat commercial pursuits. Therefore, this lesson should serve to ensure that we do not get stuck with the concept and in grand declarations of ethics, but ensure that ethics are applied and that the deontology is effective. Later on, I will suggest a solution for partnership of your organizations with the National Board of Medicine regarding the processing of data, algorithms and artificial intelligence in radiology. The 33 recommendations that were published by the National Board of

Medicine are in the document holder you were given. We called them recommendations because they were intended for the public authorities, professionals and universities and are formulated in somewhat academic terms to give them a bit more scope and the possibility of being heard. This first objective has been achieved because, as David Gruson told you earlier, on behalf of the National Council, I participate in a work group for the National Advisory Board on Ethics.

* Re-transcription of the talk

1 The National Board of Radiology which includes the French Board of Radiology Teachers (CERF), the French Radiologists Union (FNMR), the French Radiology Association (SFR) and the Hospital Radiologists Union (SRH).

Chapter II - Artificial intelligence and medicine: the official recommendations of the Medical Board - 67

I met with Dominique Pon, Director of Clinique Pasteur in Toulouse, assigned by the Minister of Digital Data in General Health Care. The subjects we are discussing this morning and which you will discuss this afternoon are obviously a part of it.

So, first and foremost, although it is absolutely necessary to mention Hippocrates when the Board gives its opinion, radiology didn't exist in the time of Hippocrates. So, I would say that Hippocrates took Humans away from the power of God and said it was useless to make individual or collective expiatory sacrifices in exchange for divine mercies. Rather, it would be better to observe Humans in their environment in order to help them. This means humans today, in the world as we know it, professionals as well as sick people. Let me tell you why I use the term 'sick people' rather than 'patients'. It is because the term 'patients' still has a connotation of submissive clinostatism. I mean the sick person who is not an individual covered with sensors and connected objects and who could send their data to artificial intelligence. I would also like you to read the entire report that is available on the National Board website. Of the 33 recommendations, I will only mention a few and maybe elaborate on them a bit.

The first point is not about the first recommendation but rather the sixth. 2 Google, Apple, Facebook, Amazon.

As time is limited, I will talk about this one. Future transformations promise to be as profound as those that came with the invention of writing and then printing. Let me remind you that the regulatory authorities of the time, which for our country was the Holy Roman Inquisition, did not fool around because they used to burn books that were disruptive to the new conformism that prevailed and which was in the Scriptures. That wasn’t all, sometimes, because it was more convenient and to prevent them from repeating the offence, they used to burn the authors and in that way there was no problem.

All of this is to say in a nice but dramatic way, that in fact, it is not enough for us professionals to remain on the banks of the digital river vituperating about the fact that we are going to be dispossessed of something in order for it to not happen. If France makes a law it will not be enough to stop it from happening. By definition, and David mentioned it earlier, there are multinational giants which are not States. The GAFA 2 members are not States, but in fact, GAFA impose their politics in the United States. The same goes for China, the companies that are developing


are not States. They couldn't care less about standards that would be purely state-controlled. You mustn't believe that the French parliament could enact a law and close the borders to the internet. Moreover, it is obvious that the countries that close their borders to the internet are not countries where personal freedom flourishes.

Therefore, there must be a public debate. Guy Vallancien mentioned the subject by saying that we talk a lot about climate. An agreement was reached by the parties on climate. Talking about the United States, as you know, they withdrew, but maybe they will rejoin. Maybe, an agreement between the parties on digital subjects is also needed so that restrictions that are public but not national could be made? At any rate, innovation won't be blocked, even if it has a high impact on the organization of professions and in particular, all the professions, including yours, that deal with images. Quite obviously, dermatologists are directly impacted, as are all the other health care professions, but maybe to a lesser extent.

Therefore, we are asking the public authorities to organize a public debate, because this subject is not just the business of experts, whether professional experts or academic experts, but rather a citizens' debate. Citizens must be able to appropriate it. Moreover, it was quite clear that during

the revision of the bioethics law, the National Advisory Board on Ethics thought they would probably produce a report on technologies. This fits in with the proposal by the National Board of Medicine to have a very general techno-ethical law. Later on I will go back to the notions of hard law and soft law to prevent a blockage of innovation.

The second point after the public debate is the recommendation by the Board that any sort of technological determinism or technological glorification shouldn't lead to an outwardly passive society that feels powerless to have its concerns heard. It draws attention to the fact that social or professional apprehensions that couldn't be express or obtain adapted responses to concerns could lead to violent rejections, like the Lyonnais silk workers, because we are in Lyon, faced with changes that are too radical, sudden or poorly explained. Right now, all the professional organizations have an educational duty to perform on behalf of our members.

We also recommend that users, patients, doctors and other health care professionals get engaged in the world of data and algorithms, without paralyzing fear or dogmatic vehemence. Professor Chang also said earlier, "Don't be afraid", even if the expression "Don't be afraid" was a matter for the pope at the UN. It is through their participation, theirs


and yours and together, in the design and development of intelligent objects and devices to meet their own needs that they will usefully guide the industrial sector because that is the underlying factor, rather than leaving it up to the laws of the market that would be imposed on them.

The subject of telemedicine, which I referred to earlier, would fit in here. From these standpoints, the professional, medical and scientific organizations and representatives must support the goals of the digital economy of health care in France. It is at this level that we really count strongly on your commitment as a structured discipline to take over the subject of artificial intelligence and think about data warehouses. I'll come back to this later. With regards to this, if you were to take this route, but it is up to you to work it out, we are willing to support you through our expertise and competencies in digital ethics. This would be so, regardless of what regulatory texts are applicable to the digital world according to the current provisions of the medical code of ethics. They also need to be reformulated or reinterpreted with regards to some articles, notably

those concerning medical confidentiality. That means the patient's privacy and not the doctor-patient confidentiality.

In more practical terms, as part of the national health strategy developed by the Ministry, recently a Strategic Council on digital data in health care was established. We don't know whether or not it will last for long. After multiple consultations in which you certainly participated

as trade unions at the Ministry, it will probably be reconfigured. We think it is imperative to have a strategic council on digital data in health care, including the subject we're discussing

now, and that in this strategic council there are professional representatives, including the liberal professions sector because you made a cutting remark earlier. In any event, we are members of the current Board.

Another aspect is that initial education and continuing education, through the devices of artificial intelligence, will obviously play a crucial role in the planning and support of a medicine of the future that is already here, and which we could quite


generally call the very near future. As of now we must train doctors according to the world in which they practice and in which technologies will have a very important place alongside clinical medicine. This is actually about not transforming a person, more precisely, not summing them up into a photo negative with an automatic system which will not only give a diagnosis, but the actions to be taken. One of the characteristics of medicine and advanced knowledge has been acting in defiance of established knowledge.

Consequently, we have our entire place to hold on to. This is so especially since Jean-Gabriel Ganascia, who is quite a key figure on the subject, actually says, and you can read it, that the dogmas by which we live can be re-transcribed, even involuntarily in the construction of an algorithm, which means that finally, if algorithm was not self-learning, it would reproduce dogmas that would not be called into question, while it is actually through defiance that advances can be made in a certain number of subjects. Chemotherapy for breast cancer in pregnant women was a fundamental restriction. It is actually because certain teams defied this restriction that there are now chemotherapies that can be used for breast cancer in pregnant women.

Another subject that will certainly cause a stir, but which is very important, is that during the demographic determination of diverse medical specializations for which training must be provided during the university course of study, and in the actual contents of these trainings, the predictable changes in professions must be taken into consideration. This concerns, not only the new

professions, delegated acts, advanced practices about which there is much talk at present, but also, since job delegation was mentioned, a certain number of these jobs that could be automated, that could be accomplished within the time periods that we estimated at five to ten years.

It won't be longer. It might even be shorter, due to devices that integrate artificial intelligence.

Moreover, Jean-Philippe, you might recall, I submitted this recommendation to you beforehand. We simply recommended taking this under consideration very rapidly and not waiting. I 'm sorry Professor Meder. I know that in fact, this is not directly the case for you. The university decided on its own about professional training, especially since by nature it could tend to see this training according to the actual needs for the functioning of institutions


and not necessarily according to the needs in the territories. This subject means a lot, especially to the National Board. Training must be done according to the practices in the territories. The territories have institutions, but also the ambulatory sector.

We also urge you, because David went over it a bit quickly, to examine the legal system of liabilities. Of course, as doctors, we have ideas concerning the situation or the dilemma, but I am presenting things in a somewhat general way. Doctors, and there are some at present, will follow the recommendations of the High Health Authority once they believe that they are not liable as long as they follow the recommendations. That is wrong, because if there is a disagreement, the fact that they followed the recommendations will, of course, serve in their defense, but they could always be told, "In this particular case, you could have gone against the recommendation which was not adapted". This is the context to some extent. A certain number of our colleagues, maybe not you, believe that as long as, acting a bit like a robot, they apply a recommendation and not even review it, that is all that is necessary. In fact, this can pose a certain number of questions when medications are removed from the prescription list,

as was the case recently with the anti-Parkinson’s medications. This came up with other subjects.

What I mean by taking that example is that a doctor will use the means of artificial intelligence in professional practice. You will follow what the machine tells you. Therefore, you will have a diagnosis and a treatment or at least actions to take. What if there is an injury? Who is responsible? Is it more important that you followed what the machine told you or your own mind because you could have chosen not to follow what the machine said? Therefore, there could be a conflict. Contrary to what David told me about the legal aspects, we are not completely convinced. I'm even convinced of the opposite, that the current state of law regulates this problem. I don't know whether Anne Laude will speak. From a strictly legal standpoint, I think it is a subject that is worth being mentioned.


The exploitation of big data is of major interest. At present, there is the national health data system which is managed by the National Health Care Data Institute (INDS), which includes the SNIIRAM 3

and the PMSI 4 data. These are public data. These are public databases. Locks were placed on the access to these data. Open data is only partially open, and in fact, this is justified to prevent the re-identification of people. Once the data can be anonymized, for example, to enrich artificial intelligence, the question is irrelevant.

However, alongside the national health care data system, there are data warehouses. These data warehouses can be found in institutions. They can also be found in archives or in databases in radiology offices. Therefore, these are exploitable data on condition that the quality is good. If artificial intelligence is nourished with stupidities, it will become perfectly stupid. Consequently, there is a real problem concerning the quality of initial data. It would be necessary to apply regulation to these warehouses if they are going to be reused, including for professional purposes.

Let us not delude ourselves. Regulations can be applied to public or private databases which could be data warehouses, but at the same time, especially with these small devices

(smartphones) you notice that the citizen or person disseminates a lot of health care data or behavioral data that are perfectly identifying, in spaces near to non-regulated platforms. Does he know or does he not care? Maybe, the person to ask is the young generation who is continuously geolocalized, who exchanges on social networks and who thinks, "My life is transparent. But I don't care, even if my data is exploited". It's not up to a group of experts to say whether it's good or bad. It's actually on this subject that a public debate is also needed.

Let's take it further. Dermatologists deal with image processing. Of course, in some cases dermatologists must palpate. There could be a satellite ganglion, but nevertheless, there are many dermatological lesions which, with an excellent picture taken with an iPhone can be sent to a database. As a result, it's the person themselves who will use artificial intelligence

3 French National Health Insurance Inter-program System. 4 Information System Medicalization Program.


devices. This could be thrown in their face, "You have a very advanced skin melanoma. You have an average of 18 months to live". Obviously, there is a real problem. Should artificial intelligence devices be open to people for use? Currently, there is an ENT specialist in Strasbourg who developed a device to take pictures of the tympanum. The pictures of the tympanum rapidly go to the database in the United States. A very high quality picture of the tympanum is returned to say whether it is normal, it's not very complicated, but to describe even lesions. You are protected from that because the devices you currently use are more complex, but maybe you should look at things more in terms of the future.

To wrap up, I'll talk about soft law. I agree with David and with what many people in this field think. There shouldn't be only hard law rules, I mean, France is also a country with a tradition of writing. When there is a problem, we make a law. The law is based on application decrees. In the final analysis we realize that the application of these decrees complicate the solution of the initial problem. I'm saying this quite explicitly. There is certainly a need for hard rules as a framework. Moreover, a vice president of the Council of State used to say: "The law should be brief and solemn. Now it has become long-winded and over-fastidious". We need to get back to a brief and solemn law that

sets the principles of fellowship in a digital Republic. Moreover, a law was published on the digital Republic. It should be left up to the regulatory authorities to issue recommendations, as that is what they're called, which consist of producing soft law. That means that recommendations can change rapidly.

Moreover, we have an idea on the subject which doesn't add another thing to produce recommendations. It is to bring together the existing authorities concerning digital data: the High Health Authority, the National Board of Medicine because we claim to have a certain quality of expertise, and in any case, a legitimacy to express ourselves on this subject, the French Digital Council and a certain number of authorities that would be qualified to produce soft law rules.

Of course, I could tell you much more about it. I hope I didn't bore you. On the contrary, we encourage you to read the entire recommendations and the arguments on which they are based, which were developed at length. Thank you. n

How can private practice

radiologists prepare for the advent of Artificial

Intelligence?

Robert LAVAYSSIERE

Vice President - FNMR


I

How can private practice radiologists prepare for the advent of Artificial Intelligence? *

would like to thank Jean-Philippe for suggesting this subject "How can Independent Radiologists Prepare for Artificial Intelligence? ". There is a certain paradox in

presenting a subject about the future when you're at the end of your career. My speech will be partially retrospective. It will be a bit about the present as well as the future. You're surely wondering what is the relationship between these two pictures? You must have noticed that in the previous talks, many pictures were presented. It's quite curious. It's at least one point in common.

The lady in the lovely suit is Lord Byron's daughter. Her name is Ada Lovelace, Lady Lovelace and historically, she is the first computer programmer. She used Charles Babbage's machine, which served to do calculations. For the record, this lady died of uterine cancer. The figure to the right is a synthetic image that was made thanks to artificial intelligence in the broad sense of the term, based on multiple pictures * Re-transcription of the talk

by Rembrandt. Therefore, it is an image of a potential Rembrandt picture, created through artificial intelligence. This shows you the capacity of this group of methods to do interesting things.

So, I tell young people. Don't be afraid, as the Vice President of the Board and the Pope have said. You have a period ahead of you that is just as passionate as the one we experienced over the past 40 years with the development of new techniques. The rubbish dispenser can serve as a dispenser for certain works on artificial intelligence, of course.

The first question to ask ourselves is actually to define artificial intelligence. Actually, it's simple. It means breaking down human intelligence into elementary functions and simulating each function with information technology. As you can see, it is not a recent concept because it dates back to 1956.

The second question you can ask yourself as well, because it is always asked,

II

Chapter II - How can radiologists prepare for the advent of AI? - 77

It is the famous "hype" to which Professor Chang alluded earlier. Is it really that new? When you look back a bit, you realize that it is not new at all because, actually, computers date back to the 1940s and that the first artificial intelligence tests followed very soon after. If you take a look you can see that there are several stages. I won't go over the left section which you will find in your files. In that way, we can go more quickly to the right to the Eliza program to which Mr. Gruson alluded, to focus a bit more on a system that is quite innovative for the period (1974), a system that made it possible to identify bacteria and to suggest antibiotics and the dose adapted to the patient's weight. That was 40 years ago, maybe more. Of course, we talked about Kasparov. We'll go over this to focus a bit more on Watson, which is still topical. We also know that a certain number of universities, notably in the United States are getting out of programs because they cost too much for what they succeed in producing. The famous game, "Jeopardy" is quite interesting because a question asked about American towns and the software answered Toronto. We all know it is in Canada. Therefore, you should always take all that is said with a grain of salt. Of course, the game Go also originate from quite a different method

from the game of chess, but somewhere, there is a similarity. It is understandable that machines can adapt to them more easily. On the other hand, you haven't escaped the buzz all over the place about artificial intelligence and the scientistic illusions that go with it that even makes promises – so, is it a form of religion – the disappearance of humanity to the benefit of robots? This is what is called the distinctive feature which has already been referred to.

There are a certain number of medical gurus, in Switzerland for example, like that university hospital in Geneva that cites radiology in particular as a technique that could disappear as a result of artificial intelligence or a well-known think tank like Terranova which also places imaging in the dustbins of History. There is also a certain number of well-known


gurus: Bouzou, Ferry, Vallancien, Alexandre, etcetera. The only one who is missing is, of course, he's cited here – Schumpeter. We won't dwell on that so, let's get back to the principle of artificial intelligence.

This pyramid represents artificial intelligence in general with stratification. As Mr. Chang said earlier, the simplest level is "machine learning". But, you already use it in your practices. It is image recognition. It is actually based on CAD 1 which also functions with enhancement via the neural networks. By the way, for the youngest of you here, when the first CAD appeared in Mastology 30 years ago, people were quick to say that there was no longer a need for doctors to read mammograms and that the second reader for the detection could be eliminated because CAD would work better. We know very well that that is not true.

Kurzweil, who is one of the inventors of voice recognition, is also one of the gurus of artificial intelligence. It's a technique some of you have been using for at least ten to fifteen years. Pattern recognition is also adapted in pulmonary CADs, mammographic CADs and others. All this is not really new. These are methods that enable machines to analyze and develop a little bit, a small spot that is assigned to them, but with regards 1 Computer Aided Diagnosis.

to specific data. What is actually more difficult is "machine learning", which is something much more complex. As Mr Chang said, it can transform a regression curve into a false sine wave. We don't know exactly what really happens inside. This machine is supplied with multiple items of information. Algorithms for which the transparency is not of the highest quality are applied to it.

After that, we get intelligence that is quite commonly used. This is what our ordinal representative alluded to earlier. This little computer that you drag along with you all the time, uses what is called weak or narrow intelligence, targeted on solutions to specific problems such as personal assistance, the autonomous car, an automatic translator or image recognition. All this is the reality. What we are more interested in is strong intelligence and this is still something of the future that is capable of developing and reproducing human behaviors. It won't happen tomorrow.

Actually, perhaps this means the end of humanity, but there are a certain number of facts to consider that I think it is important to emphasize in terms of this artificial intelligence. For the moment, there is no method to make machines reflect. It is still quite artificial. Once artificial intelligence is trained, it responds to a task but doesn't know how it arrived at that result. Moreover, it wouldn't know how to explain it to you.


That is where mathematicians' filter comes in. We have a problem that is the same as the black box. There's something in the system that is obscure, profound, and we don't know exactly what is inside. We can imagine all the manipulations that could occur, because we as potential users of these techniques have no impact on it.

Finally, there is also computing power. We are accustomed to reasoning in terms of Moore's law by thinking that computing power actually doubles every two years. We can imagine that in 2025, we will have machines that will enable us to simulate human intelligence. Maybe it will be before. Maybe it will be after. In fact, there are other computer techniques that appear and generate new problems, but you must remember that this happens slowly.

What is also certain is that this barges into everyday life in quite an extraordinary way, notably with mobile telephones. It is true that it has become a very routine means of payment. You validate with a QR code, with a fingerprint and the expense is immediately paid. Money is being eliminated. This also has an obvious societal interest which concerns the control of financial flows. By the way, you can also use devices to do the cooking, but I have a precise personal recollection. In 1984, Denis Le Bihan had written a program on a commodore 64, no-one here knows what that is any more, that made it possible to get recipes based on what was in his refrigerator. That was 34 years ago. Today we are at radiology N.N because we had radiology1.0 or Imaging 1.0 with Röntgen's hand. Next there was Imaging 2.0 with the sudden development of new techniques and slice imaging. Theoretically, we are now at Imagine 3.0 because, finally, we're trying to make the patient the focus of the matter again and sort of get away from the technical side. This is where people tell us that in the health care field, 30% of the processes can be automated, or even 60% in imaging. We're also told over and over again, and we all know it, but we have all found a way to escape it, that 50% of the doctor's time is spent on non-medical tasks. For me, this is the major challenge of artificial intelligence.


Finally, what can radiologists expect, whether or not they are independent? The term independent is not restrictive to these techniques. I think that we should keep in mind anyway, and this was touched on earlier, that human judgement must assist machines and not the opposite. So, let us take stock, where are we? We know that the development of our imaging has resulted in it being totally digital now almost everywhere. There is also an effect that is actually important and that is specialization by disease. This is actually a significant demographic effect insofar as it worsens the demographic deficit.

My generation knew how to do everything. This is no longer true at all. There is jobs delegation - it was hinted at – for examinations, for actions concerning computer functions, for information technologies. Radiologists no longer do everything themselves. They delegate extensively. There is also a general trend, which is good, it is functional integration on a site where theoretically all the modalities and all the techniques are available, a reference point in an area or several reference points in different areas. We have gone from the medical art to a profession that is a real profession with competencies and specific competencies. There is a point that I think is extremely important and dangerous, it is that we have gone from individual service in a doctor-to-patient relationship and vice versa to a mass production system into which

we were pushed and into which we allowed ourselves to be pushed also under the influence of productivity which is an absolutely detestable word and which resulted in what the Americans call "commoditization". In other words, in the end you are transformed into a McDonald's waiter.

What are the duties of imaging? These duties continue in the present, but also in the future and in an even more acute way now with pertinence. In other words, this is imaging at the right time and in an efficient way. We are expected to provide diagnostic precision that is technical, as well as to carry out the procedure correctly and in connection with the clinical aspect, which means physical security for the patient. The patient must leave in the same state or if possible in a better state after an imaging or even an interventional procedure. His mental status must be good. We shouldn't increase his anxiety. He must be informed of the results of the examination. The report must be usable. That means, it must be standardized, structured, clinically pertinent, and answer the question if there is one. The communication must be adapted, done in the desired time for the patient, but also for the clinician. Remember that we have an obligation to inform or to follow up on the effectiveness of our information. It is good to make a diagnosis of breast cancer, but the patient should also be made aware of it and fully understand as should the doctor who should ensure the follow-up so that the same patient does not return six months


later with a request for a mammogram from another practitioner who found a tumor.

Of course, there are shortcomings. We know what they are. There is wastage. It's obvious. We know that when you take just a little bit of interest in imaging errors, there are many reasons for imaging errors. There are approximately 40 cognitive biases, including about ten that are adaptable to imaging, which explain the errors attributable to cerebral functioning. Nobody escapes it. We know that the quality is inadequate. Approximately 15 to 20% of the examinations are diagnostically inconsistent. There is a shortage of specialists or super-specialists, insufficient communication with the patients and doctors. There is insufficient confidentiality, and records are vulnerable. There is also a loss of efficiency due to the loss of time. This loss of time is applicable to referring doctors as well as to us when procedures are being carried out with a certain number of almost mandatory steps that I won't list for you,

that you know and will find in your file. This is where artificial intelligence can help us in medical imaging. It can help us for data collection, prior collection with the establishment of questionnaires, the use of electronic files, automatic search for data and assistance on request. There is the good care guide, but we know very

well that the patient does not arrive saying: "I have a tumor." At the same time, we have to remain relevant. On-line programing of examinations, all those things progress slowly, the extraction of pertinent information about data that were collected, electronic transmission of consent, pre-programed protocols

and automatic search for medical histories and the comparison. Some of these functions already exist in our systems and have greatly simplified our lives and greatly improved the quality of our services. There is data processing with questionnaire analysis, aid with detection, CADs – I won't say assistance with diagnosis – automated reconstructions, automated


comparisons, interactive generation of reports and structured reports, and of course a second reading that is either systematic or following an expertise that we can request ourselves or that the clinician can request.

There is a tremendous amount of applications. As you can imagine, artificial intelligence can in fact enable us to sort files and synthesize pertinent images and data. This presupposes extremely sensitive algorithms for normal files and very specific algorithms for abnormal files. It doesn't serve any purpose to detect something if we don't know what to say about it. There are a certain number of tasks that are absolutely tedious about which Mr. Chang spoke, notably the RECIST criteria. If we should continuously do this manually ourselves, we would ruin our lives doing it or even our health. The semi-automated report is also something useful to the extent that it can help us to do an automatic scoring. This is quite useful for the discourse to be coherent and clearly understood by the corresponding clinicians.

At the base is where artificial intelligence can also help us. It can do this through quality assurance. We know very well that there may be inconsistencies in the report, transcription and voice recognition errors. It is estimated that the rate of errors can be as much as 20% at least in transcription and comprehension.

This can be extremely serious. There can be omissions.

We shouldn't forget to evaluate all these techniques either, because it is quite nice to offer them, but we need to know what purpose they serve and what the final results are. We can't trust them blindly. We must actually remain critical. It is essential. So, actually, there is already a certain number of software that helps us in our practice. In this case, I used an example by Philips. I'm not a Philips client, but I find the system attractive. If we look at an independent company, there is an automated report proposed by Icometrix which is an evaluation of the advancement of multiple sclerosis and which is quite an efficient system. It is true that it is not very exciting for you or for me, to count plaques and see whether they have changed a lot in comparison to the previous examination.

It is also obvious when you investigate a bit, when you gather information, that there are visions that could be considered as hellish for us. For example, this proposal by an American company called Enlitic, which believes that the growth of imaging data is about 50% in ten years. The result is a mass of data that is absolutely incredible, in the United States for example, 300 million images will be produced per year, which it will be useful for the machine to sort automatically with


automated detection and according to the results of this automated detection, routing to the specialist. Of course, they will rely on us, on the neural networks and the super CADs because we know that the current CADs have a few defects. Finally, we end up with three categories: screening, assistance with the decision concerning pulmonary nodules and a retrospective analysis based on collected data. Here is a possible vision of what artificial intelligence, when pushed to the extreme, could provide us with.

What are the developments? I think there is a fundamental point to remember. We are changing ecosystems. It is already the case for "clouds" which have more or less become a habit and all the techniques that actually enable the interoperability of systems and "tele-monitoring" through "blockchain". In fact, that is remote monitoring. When I say

monitoring, it is in the good sense of the term.

What is quite fascinating is to see the sums that are invested by various companies. The right section of the slide which I found on the Fujitsu website concerning genome research, which explains that not so long ago in general, several weeks were required to explore genomic data and genetics and that at present, thanks to the application of technology, this can be done in just one day with a real collection of clinical and genetic data.

As for us, for a long time it was a question and teleradiology was alluded to. There is a good potentially for development. Actually, the performances of teleradiology can be reinforced with artificial intelligence, with structured reports, the transcription and translation of reports, automatic sorting for outsourcing, – according to the type of detection, it could very well be directed to specialists – the securing of data, the interoperability of systems and the creation of experts with experts who will act on a service basis. Of course, this is with the knowledge that there is a problem with the legislations which are different from one country to the next, but this can work in one country.


We have arrived at an important chapter which, for the moment, is not in actual practice, it is radiomics and radiogenomics. At the top of the pyramid after the collection of these data, there is the possibility to implement the famous personalized medicine. Remember that radiomics is the connection between imaging and clinical, histopathological and biological data. A layer of genetics could be added, knowing that behind this, there is the risk of knowledge, but which is only statistical knowledge. The important thing is to remember that.

Obviously, it can also be used for data processing. Here, it is no longer about "big data". It really concerns "huge data". It is a major challenge for the profession. Our data must be structured, but they shouldn't be allowed to go anywhere and anyhow. Obviously, all this generates some societal considerations. Independent radiologists are maybe a bit more in the heart of the city than hospital radiologists in their interactions with the private practice doctors and patients and hospital patients. We could cite Rabelais. These slides are a bit terifying.

The one on the right is Tiananmen Square in Peking. As you can see, each person who crosses the square is subjected to facial recognition. It is definitely known who crosses the square. On the left is something different. This is Japan. It is the identification of vehicles. Here, for example, it the deviant behaviors of vehicles that is detected, thanks to the computer monitoring system.

Another societal self-reflection is should you be patient? When I say ‘patient’, it is actually in the sense

of he who waits, not in the sense of he who suffers. By doing a bit of bibliographic search, I came upon this article on an electronic medical record and curiously, in it are Bruno Silberman and

Jean-Philippe Masson. It was in 2004. As far as I know, the electronic record only caused a financial disaster. I won't name the responsible agency.

What are the other incentives? There are some that are quite valid before going from fantasy to enhanced intelligence. There is the decrease in errors, the multiple causes and the variances. From time to time you can see


the figures: 100,000 deaths per year due to medical errors and that's not all, etc. Will we really economize by eliminating the functions with a low added value? It was the famous back office to which they referred earlier; the optimization of costs by eliminating inappropriate examinations, this one certainly; the hunt for abuses concerning patients as well as doctors and then also the hospital structures. We know very well that some hospital structures, moreover, sometimes private ones, cheat. Aren't there other means to invoice procedures that are not in the nomenclature, despite the many agencies that are supposed to be taking care of it?

In fact, there is a risk in all this. It is "uberization" – a term that is a bit clichéd - or "commoditization". This means that it should go as fast as a fast food restaurant. All the same, it is a marked trend that can be observed in our patients. There is also abuse with all these robots that finally end up simulating human presence. You can make an appointment with the hairdresser and doubts are expressed. The robot is capable of saying: "I'm not very sure", and of completely deceiving the hairdresser who makes an appointment. No product has really been tested and none has been really clinically validated. For the moment, it hasn't been made, at least not to my knowledge.

There are legal problems besides the problem of "big data". In fact there are problems with the data protection law. Algorithms are used in the health care field. Who defines these algorithms? Who monitors them? Who monitors their development? If we imagine that the machine learns, what will it learn? Which direction will it take? This is a good question. We could say that ethics have become an obsessive compulsive disorder without a solution. Finally, we arrive at transhumanism and solutionism. I don't really believe in it. I will probably be dead.

I will attribute the last consideration to the photographer who took this famous picture for Givenchy. As it happens, it is Franck Horvat, a young 90-year-old man who is a very well-known photographer and whose Facebook account was closed because he had naked women on his page. As he said, "What I find saddest is that it wasn't a human who made the


decision, but software." This is what is waiting for us. This means we must be vigilant, know what is in the black box, what are the biases that were introduced, or acquired in the algorithms, but we are neither mathematicians nor computer experts. In a way, we will become prisoners, as we are in fact, of our hardware and software suppliers, notably the RISs. Could there be induced discrimination according to comorbidities or other criteria we don't know about, improper use of indicators, legal or even tariff bypassing, a gap between the creators, the industry on one side, GAFA 2 and the users, doctors and patients, with a power exacerbated by the machine and finally, questioning the management of the fiduciary relationship between the doctor and the patient? A fundamental question was also raised. What happens to confidentiality? Actually, we kind of have the impression that in the end people don't care that their data are connected by their iPhone or whatever smartphone they have.

I have great confidence in the imagination of radiologists and their capacity to act and build a radiology business, maybe not intergalactic but at least for all of France. What is the lesson in all this? It is indisputable that artificial intelligence is here. It is rapidly developing. Will we really reach 40% automation? I don't know. One thing is sure and that is, 2 Google, Apple, Facebook, Amazon.

they tend to find solutions for us, but which don't necessarily take into account our problems. We shouldn't tack on solutions like that. We need to start with the problem and we need to be heard. This is difficult all the same. The slowness and reticence are quite clear whether it is in the hardware production industry or the field of software supply. It takes years to make a change. The structures must be optimized as Paul Chang said, notably in terms of pipes, but not only that, to be able to hope to be enhanced intelligence and not artificial intelligence. We must choose our partners and the question we can very justifiably ask ourselves is whether our suppliers are up to the task.

French risk analysis software suppliers are an example. We know that these are small businesses that work well more or less and that here again it is a constant hassle to improve matters. I think it is extremely important to maintain control over data and not degrade them. Nevertheless, at the end of the day, we should remember that it is about the doctor-patient relationship. It is also in imaging. Doctors remain in control through their cognitive capacities. A robot with empathy won't come about overnight. I chose this one for Mr. Chang. I don't think that even artificial intelligence would be able to represent a democratic brain in all its complexity. n


Jean-François MEDER

President - SFR

What will Artificial

intelligence change for radiology according to the

SFR?


W

What will artificial intelligence change for radiology according to the SFR? *

hat will AI change for radiology according to the French Radiology Association (SFR)? This is a difficult question. To help me find the answer I

questioned certain members of the SFR AI work group

AI: is it change or a breakthrough? For a profession like ours which has known such changes over the course of its brief century of existence, we could assume that it is a new change. Especially since radiologists are ready. They are prepared for artificial intelligence. They are accustomed to digital data. They work with digital data throughout the day. Even the overall perception of the knowledgeable individual has changed. Before, knowledge meant having encyclopedic knowledge; today, knowledge means "what are your faculties to find something you don't know about? " Moreover, in certain American faculties examinations are done with access to the Web. The place of imaging has changed significantly. "First, the clinical examination", is a standard saying. This is

still true, but the clinical examination, even if certain clinicians don't agree, has lost a bit of pertinence because people want to go straight to the radiological confirmation. First of all, let us acknowledge certain things:

- the quantity of data is steadily increasing; - we are not the only image users; - some people and in particular administrators, would like to have a completely dematerialized radiological examination circuit;

- there is less and less of the patient-radiologist relationship in certain structures

- there are teleradiology "factories" and maybe also "radiology factories", this is very unfortunate especially since we have a knowledge, a tradition of care, a care ethic.

Is AI a nightmare for radiologists? If you believe some people, it is clearly yes. This will be dramatic because AI works as well as experts. In Radiology, an editorial reminds us that software, approved by the FDA 1 makes it possible to inform the clinician of the existence of an arterial occlusion. For Geoffrey Hilton, we should stop training

1 Directorate General of Health Services * Re-transcription of the talk

II

Chapter II - What will artificial intelligence change for radiology according to the SFR? - 91

radiologists, because in five years… A considerable number of articles about AI are referenced in Pubmed, more than 6,000 since 2017. Among them, only 7% are devoted to imaging; AI does not take into account the traditional scope of the specializations, it is interested in the care pathway, but not specifically in radiology.

For many colleagues who were consulted, AI will make it possible to improve the process of programming and completing exams (technical conditions of the procedure, decrease in the exposure to X-rays, automation of lengths of acquisition, soundproofing, harmonizing protocols). Assistance with data analysis will also be useful. Making repetitive tasks automatic will provide time for more sophisticated analysis and for the radiologist-patient relationship. Nevertheless, who will set the limit starting from which an activity is considered repetitive? The development of the profession towards more complex tasks is certainly very interesting and if the radiologist is more available this will enable him to dedicate more time to the patients and the MTM.

AI should not replace the radiologist for several reasons: 1/ the radiologist is a guarantor of the choice of examination, its pertinence and the conditions under which it is carried out; 2/ he corrects the diagnostic errors; 3/ and most of all, the radiologist is liable.

Can the radiologist become a "data scientist?" What are the activities that define a data scientist? He is responsible for managing data analysis and exploitation. He is trained as an analyst,

has knowledge in statistics, proficiency related to tools, notions of machine learning, etc. All these elements are far from being a part of radiology training. In his report, Mr. Villani wrote, "the access paths

to medical studies must be transformed, by including more students who are specialized in the field of information technology and AI ". It is certain that medical training must take into account AI, but as the members of the SFR AI working group said, "Doctors working with scientists is wonderful, but they must keep their human qualities ". They must seize the opportunity to reinforce the interactions between


young researchers and young radiologists. We mustn't forget the humanistic side of our profession.

We are going through a transition. We can admit that once the "machine" is better, reads better, the radiologist will be obliged to see his profession change. But the guidance of the inevitable transformation must be done by the health care stakeholders. Radiologists must participate in this transformation. They must reflect on the change in training, the reorganization of work and the use of the time that is made available.

What does AI need? It needs digital data, standardized data, labelled data, in particular for educational purposes, radiology reports and final diagnoses. Therefore, it needs us. Managing a platform, it is probably up to us to think about it. Labelling pertinent data, until the contrary is proven, we were trained to recognize them. Validate tools, protect

against the advent of globalized medicine (there is a tradition of care that is related to our way of living). Participate in the establishment of a legislative framework, we must be present.

In his book "Le syndrome de Garcin" (The Garcin Syndrome), Jérôme Garcin reminds us of what Charles Nicolle said to a colleague who was distressed by the suffering of a patient who had an incurable disease, "did you at least hold his hand?"

Chapter II - What will artificial intelligence change for radiology according to the SFR? - 93

How can a radiology resident

prepare for the advent of Artificial Intelligence?

Cédi KOUMAKO

President - UNIR


I

How can radiology residents prepare for the advent of AI? *

'm going to talk about the opinion that radiology residents have of artificial intelligence. I would like to thank the FNMR for inviting me today.

To start, this little presentation is based on a survey that we conducted with all the residents. These are incomplete results, because the survey is not yet closed, please take this into account.

As you see, we had 158 responses to this survey, with a distribution that is relatively homogeneous across the years of residents. I hoped that there would be many fourth and fifth year students, but there were fewer than the younger ones. It doesn't matter.

Here is a slide that I really like, because at the FNMR, this probably interests them to know what residents want to do with their lives later on. Many mixed practices, the majority want to have mixed practices, surprising, but that is it. Private practice, an exclusive hospital practice,

* Re-transcription of the talk 1 University Professor – Hospital Practitioner

this is part of what is least interesting for them. Apparently many "I don't knows" as well.

The idea behind this initial question was to identify the population that was being addressed, and not to just question people who want to finish PU-PH 1. Here, it is relatively heterogeneous so I am interested. "Did you ever complete research training?" The majority said no. Once again, these are not people who want to end up as university teachers, at any rate, not according to what they say.

The interesting question is: "are you or have you been involved in a project related to artificial intelligence?" And that is dramatic, 147 people have never had contact with artificial intelligence, 93%. There are 5 who currently have a project in progress related to artificial intelligence. After 1, 1, it is 1: "I'm not, but I would really like to". And 1 "I'm looking for a start-up to work in artificial intelligence". In general, the majority who answered this survey have not been

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Chapter II - How can radiology residents prepare for the advent of AI? - 97

involved in any project related to artificial intelligence.

Next question: "have you had training in artificial intelligence?" No, for almost 97% of the cases and this corresponds well with the previous slide. They were not trained therefore they don't do it, which is logical.

Next question: "if training was offered, would you actively participate?" They would rather like to be trained. It is up to us...

Yes, there are 25% who do not want to, but this survey has a few responses. The majority would rather like to be trained in this subject.

Question: "I don't think artificial intelligence can replace radiologists. Agree. Do not agree." The majority agrees they will not disappear. This is rather a good thing.

Next question: "In about ten years, radiologists will essentially only have interventional radiology (IR)". The majority of radiologists do not agree. We asked this question because there are quite a few tales about interventional radiology, one of which is: "you're going to disappear, so do IR because it is the only way to

2 Multidisciplinary Team Meeting

manage. At least, carry out treatment, you cannot be replaced". I don't think it is the road to take, but that is part of the legend. That is why we asked these questions, and obviously the residents did not agree with this idea.

"I think that certain fields of diagnostic radiology will not be impacted by artificial intelligence". "Yes, we are talking about interventional radiology, diagnostic radiology. Yes, they think that certain fields will not be impacted.

Among these fields, I've mentioned the details of what comes up again and again: • Ultrasound, a lot, pelvic ultrasound, obstetrics. • Some people cited mammography also, which is something I did not understand. • Pediatrics. • Many people think that radiology will have an expert activity, consultation, with participation in MTM 2.


• Many people think that digestive imaging will be one of the specializations that will be saved by artificial intelligence. Why? Simply because it concerns organs that move a lot, and having this interpreted by AI is complicated. Many artefacts, a lot of movement and most of all, there is everything related to care or postoperative imaging. Because, you shouldn't forget that artificial intelligence needs images with which to train, and postoperatively, there is nothing that resembles the standard. • Many people think that emergency imaging will also be saved, for the same reasons. • Complicated cases.

In general, the idea that stands out: go into pediatrics, ultrasound or digestive imaging, because the organs move and the cases are complex, because there will never be enough cases to train machines to interpret complex cases.

Next question: "I think that artificial intelligence is a fashion more than anything else. The changes in our practice will actually be few". They don't agree. I think that is good, because they have assimilated that it is not an effect of fashion and that in principle, this will remain and that it will even change our practices.

"I think all the fields of medicine will be impacted". Yes.

"I see this impact as rather positive". Remember this slide, because there are two questions after that, which I don't really understand.

They think that intelligence is going to relieve them of repetitive tasks that are hardly gratifying. We talked about it this morning, notably about RECIST, among other things. I will tell you what I think about RECIST afterwards.

"I'm afraid for my professional future as a radiologist". The majority are afraid. I find this quite astonishing, because that contrasts with this slide. The diagrams look quite alike, where they see this rather positive impact, but at the same time they are afraid for the future.

But a bit of hope, when you ask them if they would do radiology again: they would still do radiology again.

There is a slide that I didn't put in the PowerPoint document because I tried to make it brief. It is that just before this question,


someone asked: "would do medicine again? ". They would do medicine again, surprisingly, with a lower percentage than: "would you do radiology again? "

Two questions remain. "I think AI should be regulated so that doctors are not replaced", Yes. Personally, I find this quite dramatic because the vision I have of the thing is: "I'm afraid of being replaced, and I think that the public authorities will be able to limit the influence of this thing so that we can manage. "

And the last question is: "in your opinion, when will artificial intelligence be applied to day-to-day radiology? " • Many people think it will be in 15 to 20

years. • Many, in 10 years as well. • A few, in 1 to 2 years. • A few also, never. • Never. I don't think it can work.

I think I have finished with the slides. That was the first part of my talk; the general opinion of the residents.

I was told that I should talk about my own personal opinion. I haven't answered the survey yet. I think I will answer just before concluding. What is my personal opinion? I’m currently working on a subject concerning

artificial intelligence, about muscle segmentation with a deep learning software, which automatically segments the muscle groups to do quantified imaging, to do quantified MRI.

I must admit that I see this in a very positive light, because I've done manual muscle segmentation and it is the worst thing in existence. It isn't possible. So, I would be happy to have a machine that does it instead of me. How do I see things? There are many fields of imaging that are not explored at present, because we don't have the capacity to do it. We don't have the means to do it. We don't have the time to do it. We certainly don't feel like doing it because it is a bother. And all these things can be replaced by a machine that does in instead of us, which will enable us to both discover unexplored regions and participate in medical care again by interacting with clinicians.


Since RECIST was mentioned this morning, there is something I would like to say. I do RECIST, in training we do them manually. And when you have ten TAP RECIST scans to do in the morning session, I am sorry, but I can't attend the MTM that is taking place at the same time. Everything that is said about RECIST and which I do, in the end I never hear anyone talk about it. If there is something that can relieve me of this activity so that I can attend MTMs, I would be happy.

I'm also a bit worried though, but not extremely, because radiology as we know it today will not be the same in twenty years, that's for sure. But, I don't think we're at the dawn of our disappearance, because we need radiologists, we need someone behind the machine. Patients, not all, but some patients need a human contact to explain to them what they have. As my colleagues said, in certain fields they cannot be replaced, humans will always be needed. I am rather positive about AI. I see it a bit like a gold rush in the Far West, and I would like radiologists to be the first to arrive, that's all. We shouldn't let ourselves be overtaken.

I don't think I have much more time. I'm just verifying whether I mentioned all the points I wanted to talk about. I read many things about AI because it is very fashionable, as you can see. On the monitoring sites, not a week goes by without ten articles on AI. There

are always positive and negative things being said. But there is a sentence that I read that worried me and which I find very interesting, it is: "AI is not meant to replace radiologists, but radiologists who use AI will replace radiologists who do not use it". All I have to say is take the plunge and go as far as you can. We mustn't let ourselves be replaced.


Who owns radiological data in the framework of big data?

Laure SOULIER

Lawyer - Cabinet AUBER


F

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Who owns radiological data in the framework of big data? *

or those who don't know me, I'm Laure Soulier, lawyer at AUBER law firm. You're more accustomed to my associates Philippe

Cohen and Marie-Christine DELUC. In the framework of our business, we have the opportunity to support you in this area of your professional activity. Today, I was asked to talk to you in particular about radiological data and who owns them.

Since this morning, we've been hearing about artificial intelligence, we place ourselves upstream in relation to this: the database, in order to supply this artificial intelligence. I believe everyone has clearly understood the problem and I think things have been clearly laid out.

Current progress requires and permits large-scale collection and processing of personal data and health care data. They've become the problem, a fundamental value of the digital economy and they necessarily lead to the question * Re-transcription of the talk

which is to determine what are the rights concerning data, a key question because I am mainly addressing radiologists.

Do these radiological data belong to the patients? Do they belong to the radiologists? I would be curious to find out what a little survey would say: who thinks they belong to the radiologists in the end. Not that many. I thought there would be more. And who can use them and under what conditions?

First of all, it is fashionable. You must have received an astronomical amount of e-mails that manage personal data on your smartphones and in your mail boxes. You must be aware of the European rule that recently became effective on May 25th and which, for the first time gives a precise definition of health care data. It is there. This rule is now applicable and you are expected to have implemented the processes. You are expected to know what it is and how to comprehend it.

Here, the definition: personal data: related to the physical and mental health of a natural person

Chapter II - Who owns radiological data in the framework of big data? - 105

including health care services that reveal information on the health care status of this person. Obviously, your treatments as such, what you practice daily, are a part of it and are included in this definition that has now been established.

What happens in French law? Health care data is sensitive data, a particular data. At present, a mass of information is available and this is actually illustrated by the news and GAFA 1, and all of that deals with this subject. Today, we are on a slightly higher notch, a special notch, because we're dealing with these data said to be sensitive. They are governed by common law and there is a legal protection system.

Is it my patient or I who own of these data? First and foremost, health care data belongs to the patient. This is based on the law of March 4, 2002 which considerably extends the rights of patients on their medical records, notably on its transmission. There is the law of March 4, 2002 and the Public Health Code which specifies in the definition that I didn't use, that your radiology treatments, negatives and the interpretation are an integral part of the medical record. As a result, the patient has a right to their records and all those elements that are obviously quite important, in any case.

In the final analysis, are they the owner? Or are they simply a depositary of their health care data? Legally, what we know is that a data is without a doubt, a thing.

The problem with health care data is that it will have a close connection with the individual, and that is the source of a controversy today that has not been settled. Therefore, I won't be able to give you a definitive and final solution to the problem. For some people, there is a right of individual ownership of these data, because it is their privacy and therefore their property. Others on the contrary, consider that it is a simple expression of the person's individuality.

Even though we talk about artificial intelligence, even though we talk about technologies, at some point we always have to go back to the basics. According to these basics, the right to ownership consists of usus, fructus, and abusus, for any type of health care data, for example, the human body. A parallel can be drawn with the human body. I cannot freely have my entire human body at my disposal, I cannot give up my kidney, I cannot give up certain organs. Yes, I can donate my blood, but obviously, I can't commercialize it. In fact, what is established is that the individual cannot freely have their health care data at their disposal, they cannot sell them. In reality, as we see

1 Google, Apple, Facebook, Amazon.


it, in terms of the right to ownership, the term 'ownership' is not adequate in relation to health care data. The individual is only an agent, usufructuary of their health care data.

Currently, there are two opposing arguments in the media, but I would say, we are not the owner of our data. The proof of it is that if we're making this fuss today about the current regulation, it is to protect personal data. If it were the patient's property, we wouldn't need to protect them as we are obliged to remind you today.

However, you have this entire process that has been implemented to protect them, specifically to protect them from everything that happens through GAFA 2, everything that you see nowadays in the news where these shortcomings have been demonstrated. It is up to us now, it is up to you as health care professionals, to try to deter this problem a bit and all this stress that is present now, in any event for your patients concerning the use of data.

2 Google, Apple, Facebook, Amazon 3 General Data Protection Rules

There are means of protection. They are listed by the law. They have a right to have at their disposal, decide and control certain uses. The GDPR 3 just told you that this reinforces health care data protection. When you delve more deeply in the GDPR, I will touch on this again, I actually don't find things to be completely different from what was set by the law of 1978. The data protection law paved the way.

At present, you will see that health care data can be perfectly collected and processed under certain conditions, of course. But I think there are still a certain number of things to be done to protect users and so that you can, with their

agreement, with this work we will do together, have at your disposal, process and collect so that you can improve the health care system today, for the primary benefit of the patient. Let's put the focus back on the patient. Maybe legal matters concerning this can be addressed.

We are often asked the question: "What should I do?"


"Legally, what should I do?" My answer is always the same. First, define your medical project precisely and we will adapt, and the law, the regulation will find the solutions to enable your project to succeed. But this project must be, first and foremost, a medical one, and of course, in the primary interest of your patients.

However, can you be considered as the owner of the data? Why not? Actually, it is your handiwork, your work, you did the x-ray, and you do the interpretation. Do you have ownership of these procedures? Of course not. The patient doesn't, and neither do you. Of course, there is a need to preserve and store, therefore, you should have these elements in your possession.

Is there a copyright on these negatives and the interpretations? That is a question I'm often asked:

"It is my work. I was the one who interpreted these data. I'm the originator all the same." No, in the eyes of French law, you're not the originator so, you cannot claim copyright. In that respect, I'd like to tell you it's a draw, even-steven. Neither of you qualify as the owner of these data.

An entire procedure was implemented so that to a certain extent these data can be collected. I would be interesting at this stage

to draw a more general parallel with "simple" personal data. In the United States a ruling was made that once you have decided to publish certain personal data on a website such as Facebook, to cite the most popular one, you can't just take them back because you no longer have ownership of them. This is actually indicative that together, we should all put a system in place so that these data can be processed in an ethical and regulative manner. We should all be able to work upstream under the best conditions.

Now, you as radiologists, how do you use this database that is absolutely exceptional and which would provide a major advancement in prevention and diagnosis? The lecture by the residents just before made me shudder because I thought, "wow, the dynamics are incredibly positive, and I have the impression that the young people


did not grasp the importance of the question. I was very surprised that the young residents did not participate more in artificial intelligence. In any event, at the beginning of the first talks, I had the feeling that it wasn't really taking hold. We talk about it a lot, but are the young people following the movement? Obviously, I think so, and it was on the slides that come after. But, it is true that at the time, my enthusiastic attitude which was that I really believe there is work to be done, and that it will be in everyone's interest, was a bit dampened.

Sensitive data. As I said: health care data = sensitive data. The new regulations now radically forbid processing these sensitive data. What does processing mean? It means, even if it is only collection. Not necessarily immediately:

creating a database. That means that when you save, when you store, even if it is only your report and the imaging, you are processing data. The GDPR says: normally, it is forbidden to process sensitive data. Nevertheless, that is what I meant earlier.

Although the GDPR is now considered as an absolute protection, in reality, I don't really see it because there are a lot of exceptions as a result of which these data can be processed. I have given you a certain number of examples, the most important ones. The most important thing is that the person concerned has given their consent, and this makes the role and place of consent essential.

However, when processing is required to safeguard vital interests for scientific research, etc., it is possible to collect all these data to supply artificial intelligence which, in the future, will assist with diagnosing and focusing your professional activity on interpretation and difficult cases, and in this way improve public health. You are allowed all these elements at present in relation to European regulations.

In conclusion, as you certainly understand, I don't believe ownership is the real issue. You are not owners, they are not owners, but in any event, you can collect them, and obviously, you can process them under certain conditions.

Now, this wasn't really the subject, but we talked a bit about it earlier. For the GDPR and everything that is now applicable, I think there are two or three crucial points that you should keep in mind. Quite obviously, it is


anonymization. Individual rights and privacy must be respected and protected. If not there will be an automatic sanction. The security of your information system and verification of the host are some of the crucial elements for processing under the conditions for compliance with the GDPR. Obviously, it is a crucial element.

I would just like to finish up and go back to what was said this morning about holding out a hand to your fellow men. Because you call on me often for expertise, I get feedback and I see many cases where there is a conflict with your patients, where patients blame you for a certain number of things. We are now at a turning point.

I mean, you have GAFA, huge structures with huge financial means which, without knowing the person, will exploit their data. As for you, you are in another category. That means you have the possibility to interact, convey and explain to the patient why you will need to exploit their data, for what reason. I believe this is your strength at present, in relation to these structures. It is not financial, it is still just human.

And from the feedback I get, it is one of the main keys for the advancement of artificial intelligence, in any event, in the field of radiology. Therein lies your strength. And saying, "No,

we are not afraid of artificial intelligence, because you have this additional strength: dialogue, and, in my opinion, it is not enough to hold out a hand, rather, you should take your patients' hand to explain why you need to exploit their data. If they give their consent, you will take even fewer risks since the project is shared. It will be built with them. A bit earlier we talked about the necessity, in my opinion, to envisage charters within institutions, specific information concerning the fact that, yes, data can be exploited.

If you work in harmony, the legislation will follow you. There comes a time when you have to speed things up. Today, we have established legal rules that enable you to do a certain number of things. Let's adapt to the changing circumstances by taking their hand, there is no need to be afraid. In any event, legally, we will help you to establish a structure to enable you to conduct your professional practice without any fear. On the contrary, you will have highly developed means by which to serenely conduct your practice.

Moreover, I hope that I myself can benefit from your experience to conduct my own under the same conditions. Thank you.

How did Artificial Intelligence

in health care become a geopolitical reality?

Florent PARMENTIER

European Geopolitics Expert Sciences Po Paris


H

II

How AI in health care became a geopolitical reality *

ello everyone. I would like to start by thanking Mr. Masson for his invitation, and his team for the organization and quality of the talks this morning, which

were all different points of view, but through which we were able to tackle many aspects.

The approaches that were used this morning were diverse. And now, we'll take a look at the geopolitical approach. To be precise, there is something a bit contradictory, counter-intuitive in the expression "artificial intelligence geopolitics": geopolitics is concerned with inertias, large territorial masses, History. While on the other hand, artificial intelligence refers to technology, instantaneousness, modernization, etc. There is almost a time agreement that is not there, in some ways, between something that must be very inertial and something that, on the contrary, insists on rapidity and change. It is this contradiction that we will try to resolve in this talk. * Re-transcription of the talk

I will also try to explain, and I don't think I will be the first in the room, that contrary to what Laurent Alexandre says, Europe also has advantages that it is quite capable of mobilizing, on condition that it has a bit of vision. This is also what I will try to present, but I won't spend time on this point now.

I wanted to start with a quote from Vladimir Poutine, a certain statement made in September. It is always interesting to listen to someone about whom you can say a lot of things. But one of his characteristics is that in spite of everything, he has quite a powerful vision for his country, which can be absolutely criticized, concerning Russia as a power... I quote: "huge opportunities and threats that are difficult to predict, the one who becomes the leader of this field will be the one who dominates the world. "

Chapter II - How AI in health care became a geopolitical reality - 113

We could ask ourselves: is there an idea of military domination? This cannot be entirely excluded considering the personality of the Russian President. Or is it rather about economic domination? What is interesting is actually to see that the share of businesses and investments that AI concern in terms of security is variable. Obviously, it is less in Europe than in Israel or Russia. We have something like that which is interesting, although Russia is not one of the leaders in AI.

Russia is hardly ever present in this setting. But it is also interesting because Russia has a GDP 1, in any case, a defense budget, that is only 10% of the American budget. It has a defense budget that is globally that of France or Great Britain. But if we talk in terms of projection capability, etc., Russia appears to be well beyond its real economic strength. The fact that Russia, that the Russian President is interested in artificial intelligence, also makes us say that in terms of health, it is worthwhile to do it as well. This presents a certain number of challenges and interests for Europe.

There are maybe three common beliefs about the connections between geopolitics and AI in relation to health care. There is the idea according to which "technology has no geopolitical impact besides a military one." Obviously, if you have a missile that can strike a partner,

it's not the same thing. But, in reality, yes, and moreover, this is part of what Deputy Joachim Son-Forget is working on a lot in the framework of the Global Variations think tank (www.global- variations.com). Technology has a geopolitical impact, we will see why, notably on health care. I will return to that in a few minutes.

"The technological field is independent of politics and therefore of geopolitics." Once again, we should reject this idea. We must remember that although in terms of defense technologies, Russia and Israel, for example, are among the leaders, if Estonia also seems to be a major country in terms of cybersecurity, it is not completely by chance 2. We must realize that again, technologies are often related to the geopolitical questions.

And then, there is the idea that sometimes "AI is a technology that develops in an autonomous linear way." We were reminded this morning that it developed in the 1950s and then we alternated between periods of forgetfulness and sometimes periods of enthusiasm. We can't make up our minds. We know that it is advancing, but it is quite possible that other exogenous technical progress, in some ways impedes the advancement of AI. Maybe, we will reach a limit. It is difficult to always be certain.

It would probably be interesting to say at this point that to study artificial intelligence

1 Gross National Product. 2 See Antoine Picron, "L’E-stonie : modèle d’un état plateforme e-gouverné" (Estonia: a model of an e-governed platform state), Institut Sapiens, 11 July 2018, https://www. institutsapiens.fr/le-stonie-modele-dun-etat-plateforme-e-gouverne/

http://www/


in relation to health, in reality there are at least two reading grids that are complementary: as we said, a geopolitical approach is the relationship between space and politics and it is very often described as the conflicting powers on a given territory. Inertias are a part of geopolitics. We will examine the structuring data. However, they have an influence, but they are not determining. It is obvious that each country also has its own way to design their borders. Here again, certain points could be emphasized.

Health care geopolitics is a relatively recent fact in terms of history. Later, people talked about health care geopolitics to evoke the major pandemics that have marked history, such as the black plague or Spanish influenza. As of the nineteenth century, there was an entire historical movement that consisted of ensuring that health care became an international issue. And for a long time, it was a lot less of an international issue than a local issue between a doctor and his patient. But we will see that there is this power, this reading grid. There is a trend to geopoliticize the question of health care. It's a trend that we will observe and which, in my opinion, is important to remember.

On the other hand, we have a geoeconomic approach. The geoeconomic approach is the analysis of the economic strategy of States and also these major

3 Google, Apple, Facebook, Amazon. 4 Baidu, Alibaba, Tencent, Xiaomi.

groups. Today, we haven't talked much about the major platforms that take an interest in these health care questions. When we say platform, obviously, we mean GAFA 3 for the United States, BATX 4 for China, leaving Europe as a space without strategy, faced with giant institutions.

To illustrate these differences between geopolitics and geoeconomics, to show that they are complementary, you can take Estonia as an example. Estonia is a very small country with a million and a half inhabitants, but it is the country whose European commissioner is in the digital field. It is a country that is quite on the cutting edge in several technologies and regulations. It is notably a country that wants to create its own crypto-currency, the estcoin.

If you're on the side of geopolitics, you'll say: Estonia has a huge neighbor, Russia, which is hostile. Therefore, infrastructures competences, etc., should be developed through quite a liberal framework, starting from the 1990s. In terms of AI, there is already a possible regulation concerning the autonomous car.

But if you look beyond this geopolitical and geoeconomic aspect, it is also a country where the Soviet Union had established a cybernetic center starting in the 1960s. They also build with what they have. It is also interesting to note that the economic approaches


and the more structural geopolitical approaches can be combined. This is actually what it is about when we address the subject today.

I won't linger on these different points. Actually, through these technologies, geopolitics and geoeconomics, it is the essential question of sovereignty that is asked, sovereignty at the political level. We spoke about sovereign clouds under the former President of the Republic, Nicolas Sarkozy. Next we talked about questions of sovereign platforms.

And there is this idea that there are new stakeholders in the health care field. This is what makes artificial intelligence possible. Beyond the patients, doctors and institutions, there are data collectors, data hosting services and data scientists. The idea is that the health care field is expanding with artificial intelligence, or rather the AIs.

A principle that should be born in mind, at any rate for the positioning of Europe, is that: those who control the speed determine the standard. In other words, it is not only being the biggest stakeholder that will make the difference, it is often

by being the first stakeholder that you make a certain number of points. This is interesting, because the standard is a key issue.

It is a key question notably for Europeans, and beyond that, the French stakeholders.

There is a small map in your file that I found on the Diploweb site (https://www.diploweb.com/Geo- politique-de-l-in-

telligence-artificielle-une-course-mon- diale-a-l-innovation.html) on the international innovation race through artificial intelligence questions. There are some good things about this map. We could go over a certain number of points. You can see the extreme strength of the United States and China, but I will try to show that Europeans also have a role to play.

Here is just one figure on this point. There is a recent report by the consultancy firm, Roland Berger which cited the 3,600 most innovative start-ups in AI applied to business development. Of course, the United States has 40% of the innovative start-ups in the field, China has 11%. But if you combine all 28 States of the European Union, we nevertheless have 22% for


the European Union, which is a significant quantity. Obviously, it is not the pole position, but for a continent that is supposed to be completely behind the times, obviously we have a few strengths.

I wanted to show you the general approach. And now, the real health care challenges. I won't linger on the subject because it was presented quite well this morning: AI in health care is assistance with the medical decision. It is better patient monitoring. It is surgical assistance for robots. This link between artificial intelligence and robotics is developing. Moreover, these are things you have already seen.

I think the distinctive feature of AI in relation to health care is that products won't be in competition as they are now but rather the ethical and cultural concepts of health care. That is a highly determining factor. The interview with Emmanuel Macron for Wired, the trendy Silicon Valley newspaper, was obviously rather positive in relation to artificial intelligence, but it didn't fail to emphasize that AI will challenge our democracies and our collective preferences. From this standpoint, it is important to remember that beyond products, it is health care concepts that are in competition.

You know that we can now take an x-ray with a mobile phone. Obviously,

this will again have strong social effects in certain countries. When you think of the single child policy in China, this type of x-ray seems potentially dangerous, and will produce generations of boys ad vitam aeternam. And there is this Indian proverb that some of you probably know, which says that "having a girl is like watering the neighbor's garden." We can well imagine what affordable ultrasound would make it possible to obtain with a telephone. That would actually have significant social effects.

This fits in with the idea that alongside technologies, there are social uses of technologies that are important factors. This is why I say, health care concepts are in the balance. Health is something that is eminently cultural. It is even more important from this standpoint for the French, France as well as Europe, to be able to promote their own concept.

I won’t go over the ethical dimensions that were mentioned this morning, nor the sources of AI because all this has been documented. What will be interesting now is to see what regulation can do. Here again, we must remain lucid about the fact that between Google AI and the media law, maybe Google AI will have an advantage, or ways to make progress. The Amazon AI poses a direct threat to our competition


law. DeepMind and Baidu have artificial intelligence software which will have their own impact on the Public Health Code. Maybe this is what we should be working on, because this is part of the major files.

We should remember, I'll do this quickly, that AI is both very connected to digital development and, of course, very connected to the questions of data, as the last speaker mentioned. We should emphasize a certain number of points. Most important of all, AI will redesign work just as the artificial intelligence interns think it will.

I won't expand on the subject because I think there are other people who are probably in a better position to talk about it than me. I will stress one point just a bit. The AI-robotization link comes up in relation to questions concerning autonomous cars. When all is said and done,

in the past a car was essentially a combustion engine. In the future, it will be an operating system. The question is how will the different European stakeholders invest in this field? I'm talking about France and Germany, of course.

In terms of AI and militarization, there will also be interesting developments. It's not hard to imagine that the wars of the future will be wars without soldiers, with confrontations by drones or cyberwarfare. All this is quite grim, but it is worthwhile to keep this type of development in mind.

I would like to end this talk with a few prospective elements. Will Europe be up to the mark for AI in 2030?

I'll make a few points on that subject.

What is interesting is that the subject was addressed in a column of the American magazine Forbes: the seven AI powers. They are the United States, China, Russia, India, Canada, Israel and Estonia. What is interesting is that the European Union is not even mentioned, neither is France or Germany. This raises questions. Estonia is there, and that gives Europeans a clue.


If Estonia is among these top seven countries worldwide that might have an influence on AI, it is maybe because Estonia works harder than the others, for example, to establish a framework for the development of the law concerning autonomous cars, as I mentioned before. This means that even a small country with a population that is smaller than the city of Paris, even a country like Estonia has their say, through their capacity to regulate and find new regulations. This is where Europe has a card to play. And this is despite the fact that our very ecosystem is more difficult, in fact we will get to that.

Maybe, we should also start with the principle that health care in 2030 will not be faced with the same problems as today. There will certainly be massive changes in the treatment revolution, in having e-patients who always have more demands, and who are more and more expert ready to ask radiologists questions simply after reading a few newspaper columns. There will be questions on financing revision, questions on governance that will change profoundly. I won’t get into this, but it's a point you should bear in mind in terms of long-term planning.

I'm going to go off on a tangent for a minute and talk about energy. Why? Because, energy, for the specialists in the sector, has that capacity like artificial intelligence, to transform a sector. If you don't have energy, you can't increase your wealth. Although

at first glance, energy seems to weigh only 2, 3, or 4% of the GNP of a certain number of countries, in reality, if you don't have energy, or if you don't have that 1% of petrol that your GNP costs you, a good part of the economy collapses. Artificial intelligence will probably be like that in the future. It is already similar.

This means that Europeans can probably find correspondences for artificial intelligence in European energy policies. It's interesting, especially for the European gas policy, because this is where the issue is most acute. In Europe, we have had a very active debate over the past decade, to find out how extricate ourselves from the dependence on Gazprom. It's an important question, but which is not necessarily asked the way it should be. There are different tools.

• First of all, there is the question of

interdependence which is a political relationship.

• There is the question of diversification.

Can we buy from other partners besides Russia?

• There are the questions of alternative

energy supply sources.

What is important in this framework, is the question that one of the top specialists


of Cambridge, Pierre Noël asked. He said, in substance, that the problem is not Europe's dependence on Russia. The problem, according to him, is that Europeans have not succeeded in coming to an agreement in order to create an integrated European market. Once you have an integrated European market, the situation is no longer the same. As a result, Europe as a buyer would become almost stronger than Russia as a producer. It is an interesting point that we should remember, because we are saying that we have no capacity to regulate GAFA or to ensure they comply with certain principles. At the same time, it is obvious however, that we've been asking ourselves these questions for a long time now, for over a decade. The analogy is true for interest.

One of the important points is also key factors of development. When we talk about long-term planning, we must first take stock of the situation, see what are the key factors of development. I won't go into the details now. We can probably leave that for the questions. Then, we need to see what the likely scenarios are and which ones are preferable. In this setting, the question is that of economy, structuring the field of

stakeholders. It's also a geopolitical question, that is, does the European Union want to move towards a form of autonomy or be totally dependent on other countries after all? The problem is that if we depend on other countries, we don't necessarily have the same ethical frameworks, and we will import unethical solutions with special difficulties, as David Gruson very clearly explained this morning.

There is also a technological question. We mustn't forget that artificial intelligence is one technology among a certain number of others, that the quantum computer is also a possible disrupting factor, and that in terms of

disruption, we haven't reached the end of the story yet. Don't forget that Kodak and Nokia were the champions in their time, and that today they're not very strong. There is no

guarantee that in about ten years or so all the GAFA will be in the brilliant shape they are today, it is quite likely, of course, but it isn't guaranteed.

Very quickly, here are three scenarios to illustrate this point. There is the scenario in which


Europe is marginalized, more and more dependent on platforms and has no real positioning. You know that South Korea is a country with a real market positioning. Israel has a real market positioning. What the Europeans lack is market positioning, in other words, a strong message to transmit.

There is also fragmentation, because we can't be efficient from the European standpoint with 28 different policies and countries that don't communicate. That simply makes no sense. On the other hand, collectively, the European Union has 22% of the important international start-ups in the sector, which is far from negligible. We could see this as a form of marginalization.

We could also envision something more voluntarist, for example, with the creation of a European DARPA 5. DARPA, is the American defense agency that is there to produce a certain number of innovative projects. One sentence summarizes DARPA; these are the words of a former president who said, "If ever you fail by less than 80%, it means you didn't do your job. Your job is to fail because by failing, if you have 10% of things that completely smash all the codes, then you would have amply won." It's a bit the mentality that is lacking in Europe and that Europe hasn't necessarily embraced.

5 Defense Advanced Research Projects Agency.

On the other hand, the geopolitical, geoeconomic AI world is not limited to only platforms or GAFA. There are research laboratories, start-ups and large firms. The day when the major German car manufacturers really start using AI and autonomous cars, we might see a more profitable shift to the European States. Once again, the question is how to implement a pertinent ecosystem. That is the scenario where with a bit of will power there is a comeback for Europe.

If we are a bit less voluntarist, cooperate a bit less, we must expect something else; a sort of salvation from one of the European economic powers. One of Europe's strengths is its capacity to set international standards. It is a bit its DNA so, that means finding an agreement for 28 States and


finding a common rule. This is one of the areas in which the European Union has had some success. For example, the fact that it is the world leader on climatic change, even though it is not the world number one polluter attests to this. There are possibilities. Is the GDPR 6 a means for Europeans to affirm an ethical standard and adopt a way of thinking and new regulations? It is quite conceivable, quite possible. It isn't written in stone either. We actually need to see how that goes.

I think I'll end on that note. Thank you.

Applauses

6 General Data Protection Rules

Will Artificial Intelligence

change the patient-radiologist relationship?

François BLANCHARDON

President - AURA FRANCE ASSOS SANTE regional association


F

II

Will Artificial Intelligence change the patient-radiologist relationship? *

irst, I would like to heartily thank the President of the FNMR for having saved a little place for us, patients, users, at this very interesting

symposium on artificial intelligence.

When we ask a user the question about artificial intelligence, it doesn't mean much to him right away. But when you dig a bit deeper, there are a few reactions that are rather positive, but also quite interrogatory and this is what we will discuss.

I would like you to know that today I am representing Alain-Michel Ceretti 1, who is also very happy and who sends hello and his best regards.

Mr President, in your summary there is a very important sentence, the last one where you specify that you will always ensure that the doctor-patient relationship is given priority. We find this reassuring because it is an extremely important matter for us. * Re-transcription of the talk 1 President of France Asso Santé

Before starting my talk, and quickly presenting France Assos Santé, I would like to ask a question. Can medical data alone establish a diagnosis? We will see at the end whether there is an answer to that question.

For those of you who don't know, France Assos Santé is the name chosen by the National union of authorized health care system users associations which represents health care system patients and users and defends their interests. It is a mission that was officially recognized in the framework of the Public Health Code through the health care system modernization law of January 26, 2016, which created the National union of authorized health care system users associations.

France Assos Santé was created on the initiative of 72 national associations that were the founders of this association. It is a part of the mobilization of inter-association groups that you undoubtedly know, which took place over twenty years

Chapter II - Will artificial intelligence change the patient-radiologist relationship? - 125

ago to build and ensure the recognition of inter-association users.

Our goal is to ensure that users' vision of the health care problems with immediate implications for them is expressed in a strong voice, but also to ensure that their expertise is recognized, expertise that is wrought from experience - we often say we are experts in experience – so that we can contribute to the health care system. It is not about simply asking us whether or not we are happy, about what is happening, but rather to acknowledge that we have the capacity to analyze and assess pertinence and professional practices, like pathway indicators or patient experience, for example.

I would like to go back a bit to the doctor-patient relationship. Between the end of the nineteenth century and the beginning of the twentieth century there was a great doctor who was

Prof. Louis Portes President of the Medical Board - 1947

the first President of the National Board of Medicine, Dr. Louis Portes who said something like this, the patient is only a toy, almost completely blind, very sore and essentially passive, and who only has a very imperfect knowledge of himself. And yet, he was the first doctor to talk about the doctor-patient privilege, that trusting relationship between the doctor and his patient which is intimate, bilateral and protected.

This marked the emergence of the first completely unbalanced patient-doctor relationship, with a significant predominance of the role of the doctor in this paternal alliance, of course, and a patient who, as his name indicates, is very passive and very patient, and completely attentive to his doctor, and who won't often say what he feels and who accepts or rejects his treatment, the explanation of his diagnosis, without necessarily giving his own opinion. At that point, we're at the beginning of the 20th century.

And then there was a change, notably at the end of the twentieth century with the development of chronic diseases that affected young subjects who did not want to remain passive about their disease, who wanted to be participants in their health care and participants in this patient-doctor relationship.

With the advent of new technologies, information and communication, this made the patient more active in his


care. He is informed, autonomous, has the benefit of a trusted third party, and is responsible, competent and engaged. He is a care organizer, participant in care and a co-decision-making citizen. This marks the emergence of user representation. Finally, we see the patient rise from this imbalance between the health care professional and the patient, in the doctor-patient relationship, to a more balanced relationship at the end of the twentieth century where they both work on a treatment strategy, a strategy for the treatment of the disease.

Then, in the 21st century a new participant arrives in the guise of connected objects. There is information as well, which is transmitted by the Web. The relationship is no longer bilateral between a doctor and his patient, but rather a three-way relationship with the advent of an additional tool. Either the Web or a connected tool takes more and more place in this relationship between the patient and the doctor.

Today, what is its precise place? Is its place identical in any consultation, in any relationship between a

doctor and his patient? I think we haven't yet been able to establish a real balance in this three-way relationship which is now established within the doctor-patient privilege. But we all know now that the patient gets information from the Web.

He will also try to find connected tools related to the monitoring of his disease, either on the instruction of his doctor, which would be

the best solution, or by buying it himself since there is a rapid development of these connected tools, for which no-one knows the origin or which learned society is

behind them. We don't really know how health care data will be processed either. It was mentioned earlier and I will get back to it. Health care data is a key element in the entire development of these connected objects, these connected tools, and of course, in relation to artificial intelligence.

Finally, today, what is important for users is listening to the patient in this doctor-patient relationship.


A study conducted not so long ago, at the beginning of the twenty-first century, that will probably make you mad, says that the time the doctor spends listening to his patient during an appointment is thirteen seconds. At the same time, what the patient expects the most in this relationship is the doctor's attentiveness so that he can correctly establish their diagnosis and correctly adapt their treatment.

What should be done, especially in terms of the role of radiologists? Because, when we as users go to the radiologist, the first question we ask is, what are they going to do to us? It is often frightening when you go to the radiologist for a procedure. The first thing is to reassure, be present before the procedure, explain the examination that will be performed.

The interview and the clinical examination should also be summarized, brought to a conclusion. Quite often, we are frustrated because we leave without even getting feedback, maybe because time is needed to explain and examine the data. But we are often frustrated when we leave the radiologist's office. And yet, we need to have the first information immediately concerning what was more or less discovered.

Finally, it should also be ensured that the patient has clearly understood, because in a highly emotional moment when the patient sometimes expects the worst, often they don't understand

at that precise moment, what their doctor, and notably the radiologist tells them.

How will artificial intelligence change the patient-doctor relationship? We won't go over all that again because you already talked about it at length, but these are the positive points that users emphasize with the use of data: to improve radiology procedures, to improve the establishment of a diagnosis.

I believe there are two things to remember. One is that artificial intelligence will shorten the waiting period for appointments with radiologists. It is extremely important to avoid late diagnoses because we know how late diagnoses can then have a negative impact on the progression of a disease.

The second thing that is extremely important is also to avoid certain care. This is called avoidable care pathways. At present, an x-ray can be wrongly interpreted and a patient can be sent on a care pathway that could have been avoided. It means trying to have a better quality in terms of diagnosis assessment.

For all that, will artificial intelligence be capable of having related data, besides medical technique? Or will it be able to take into account the patient's socio-cultural environment and enable the doctor to provide more personalized care? That is the question that


was asked in the beginning. Finally, is it that only medical data can provide a correct diagnosis? Or isn't it data interpretation as well as the personalized relationship on a whole that is established with your patient that can correctly enable the establishment of the right diagnosis? I think ideally this is it. In the final analysis, will artificial intelligence replace the doctor-patient privilege? Or will this third participant, who is a technical, computerized participant, take the step concerning the relationship between the doctor and the patient?

I would like to say no. It is simply something that will help with the diagnosis, but the diagnosis can only be made correctly with attentiveness and a good relationship between the patient and their doctor.

I used the recommendations of the CNIL 2 which highlights that the patient must give their express, free and informed consent for use of their health care data. If this is not the case, in order to be exploited they must be anonymous so that they cannot be traced back to the person. The level of patient information on the use of their data will have to be improved.

Do users agree or disagree with the processing of their health care data? When the question is asked about what happens beyond health care data, we might also wonder, are patients 2 Commission Nationale de l’Informatique et des Libertés (French Data Protection Agency)

willing to participate in research studies, for example? And when they are asked the question, overall, the response is positive. They agree very easily, once they are given an explanation of what will happen, and once they are given an explanation of the purpose of the results. There is this essential information that concerns knowing what will happen to their data, and how these data will be protected. Apart from these two essential bits of information, the patient is completely willing to allow his health care data to be used, but of course, they must be protected.

Why should users be made aware of their responsibility concerning health care data? Let me give you an example that was reported to me quite recently by a patient with AIDS. He made an appointment with a dentist and did not declare his disease at that time. When he arrived at the office, the consultation was denied just because he was


HIV-positive. This is very shocking, when it is told in this way, but finally, if we ask ourselves some questions, it is probably also to protect the other patients that the doctor made that decision. But this raises the question of knowing how the equipment he uses is sterilized.

In short, this means that when health care data are not protected and they circulate in this way, either in the medical sphere, or outside, like in insurance, the professional milieu or the social milieu, it is important for the person, and it can make them very angry to know that their health care data were not protected. There must be a wide-scale effort to make users aware of their responsibility concerning artificial intelligence. There must be protection and of course, legislation to provide a framework for the use of artificial intelligence.

The patient's living environment must also be a primary concern, because the question at the beginning was: "can medical data alone, etc.… ". I won't answer this question, it is up to you to answer, but the life of the patient and his living environment are at least as important to enable the

establishment of a diagnosis.

Users must be allowed to assess these new professional practices. Governance was mentioned earlier and users must be able to give their opinion on what will be built with regards to artificial intelligence so that they can assess whether the framework in which

these data will be established and the work on these data with regards to artificial intelligence are protected for the users.

In addition, it should be ensured, and this is very

important, that artificial intelligence does not cause inequalities. It is very important, as is the case in other sectors, notably the banking sector, which I'm very familiar with. I used to be in the banking sector, so, I know a bit about that, because we work a lot with artificial intelligence. This concerns data-based marketing where we will build targets. The targets that are least profitable will no longer have access to an advisor, and only people who are profitable for the banking establishment will be able to have this type of access. We don't want that for the relationship with the doctor. Having access to a doctor shouldn't be


related to cost.

Artificial intelligence should not be the cause of inequalities, neither in terms of costs nor in geographical terms, because, as we know, our territory has inequalities in terms of geographic access to health care. Maybe we will be able to use artificial intelligence in large centers first, with the risk that in other geographic areas it arrives, not in thirty years but in fifty or sixty years.

It should be ensured that those who need it are given use, and while this is not necessarily easy for everyone, it should also be ensured that health care data are preserved and secured. It has been talked about at length.

To finish, here are two little diagrams. Two little robots: the last one says: "if I had feelings, I would be frustrated with them. We are still a long way from human intelligence after all." Yes, next to artificial intelligence, there is emotional intelligence, relational

intelligence, which are the bases of our society, and which must be maintained.

Finally, about this three-way relationship; the patient, the doctor and artificial intelligence which is presented in a diagram. We must get to the point where we can find a balance between these three participants in the patient-doctor relationship.

Thank you.


What manufacturers think


T

II

Aïssa KHÉLIFA AGFA HEALCARE IT

Enhanced intelligence: the next frontier *

o take into account the time limits, I deleted many slides so as not to repeat many things that were already said. This way it will be shorter.

Here are four questions

by way of introduction. These are the only things I will address, I won't address what follows.

• First question: how should we

handle the fact that the rarer the case, the more the volume and type of data required are important?

• Second question: what is the

impact of RGPD 1 on data collection projects with regards to deep learning?

• Third question, after the Cloud

Act of March 23, 2018, can we still entrust these data to

* Re-transcription of the talk 1 General Data Protection Rules 2 GAFAMI: Google, Amazon, Facebook, Apple, Microsoft & Ibm

American companies? Obviously I'm talking about GAFAMI 2.

• The last point will be a problematic

question: should we talk about artificial intelligence or enhanced intelligence? You will see that the nuance is more than just semantic.

The volume is inversely proportional to the frequency of cases. The rarer the

Chapter II - Enhanced intelligence: the next frontier - 135

cases, the more we need volume and the more complex it is to process the data. However, in the example of tuberculosis, in the framework of a study we conducted, we obtained a significance level although only 10,000 negatives were processed. That is a small quantity of data in deep learning, and as you can see sensitivity levels reach 95%, specificity levels are above 75%, and the area under the ROC curve is above 0.9. We have all the criteria to validate the fact that the enhanced intelligence solution is satisfactory on a clinical level. 10,000 data is 10,000 files. That is a very small quantity. The question is that when you want to work with rare data or complex pathologies, heterogeneous data will have to be chained while ensuring patients' anonymity, of course. In theory, data must be anonymized and chained before arrival in the data warehouse.

In practice, the motors of data anonymization are located in the servers, and the data are anonymized and chained after their arrival in the warehouse. For example, for Watson Health the nominative data leave from Watson Health clients to the United States and are then anonymized for processing.

A second case concerns situations where we want to chain other things besides imaging data, such as data from the patient's record or data from genomic

3 Computer Aided Diagnosis.

sequencing, we find ourselves on multiple bases with multiple identifiers, and we therefore have to fusion all these data at some point. Where is this done? How is it done? What are the solutions that are envisaged?

How do we handle acquiring consents, not only on imaging data but also on genomic data and patient data? And how do we ensure that these consents are convergent so that they can be appropriately processed? These questions are open today, and these are the problems for which neither you nor I know the appropriate solutions.

Second point: what is the impact of the GDPR? I see two impacts: the first is when enhanced intelligence solutions are used. Should we ask for prior consent to use a CAD in the future 3? When radiologists are going to use the solution about which I was talking earlier, for automatic, automated or assisted diagnosis of tuberculosis, will they have to systematically ask for the patient's consent? We might think so.

But the problem is even more obvious when the database is being built. To illustrate this I'll use a study on pneumonia from Stanford which


was published last year. It's a study that the GDPR would probably not authorize if consent management was strictly interpreted.

You must certainly know about the study, it is about the detection of pneumonia by x-ray using a deep learning tool. They used a base of 112,000 chest x-rays managed by the NIH 4. The algorithm integrated the 112,000 examinations in six weeks, it can be fast. The results are irrevocable. I'll quote the sponsors of the study. "We are developing an algorithm that surpasses the performance of radiologists in the detection of pneumonia, based on chest x-ray negatives." At least the message is clear.

• Was there prior consent? Yes, all the

data came from the NIH, and all the patients gave their consent to participate in clinical trials.

• Is it a clinical trial? Yes, it is clearly a

clinical trial. 4 National Institutes Of Health.

• Did the patients give their express consent? No, they did not give their express consent.

Consent according to the GDPR must be express, positive, intelligent and enlightened. None of the patients in the data, some of which were more than twenty-five years old, gave their express consent for the study on artificial intelligence. That

means that with a strict interpretation of consent management, we are in very great difficulty. Maybe practice will change, but maybe it won't. We know that in a certain

number of cases, we chose the most rigid rather than the most intelligent.

The third question after the Cloud Act, which means Clarifying Lawful Overseas Use of Data:

Can we still entrust these data to US companies? The Cloud Act is a thirty-two page act in very fine text, which is very infrequent in the United States,


which was enacted last April and which says this in the main article, "a provider of electronic communication service or remote computing service shall comply with the obligations of this chapter to preserve, backup, or disclose the contents of a wire or electronic communication and any record or other information pertaining to a customer or subscriber within such provider’s possession, custody, or control, regardless of whether such communication, record, or other information is located within or outside of the United States." At least this has the merit of being clear. Quite obviously, the Cloud Act concerns all American data storage and Telecom operators. Moreover, they all applauded because the act actually clarifies the United States' position.

What does that mean? That means we could have conflicting doctrines, notably for an American operator intervening in Europe and who would therefore be subject to the GDPR in Europe. Quite obviously, this provision is completely contradictory to what is provided for in the GDPR.

The problem is that with regards to deep learning, almost all the algorithm suppliers are North American, and those who are not, are Chinese, which is probably even worse in terms of data protection.

How will we handle these conflicting doctrines between the GDPR and the

Cloud Act? If we want all our health care data to be able to go to the American federal services, we simply don't have to reflect on this question.

My last point addresses the question of whether it is Artificial intelligence or enhanced intelligence.

Since this morning, we have been using the terms almost without distinction. However, the terms are not indistinct.

• Artificial intelligence tries to replicate

human intelligence.

• Enhanced intelligence tries to provide tools, which in combination with workflow tools and in combination with processes brings about an improvement, an amplification of the work produced by human intelligence.

What does that mean? That means that at the center of the approach, as you saw this morning in Robert's talk, first you have the building of a data warehouse.


That's the first point. Next, I won't go back over it because Paul Chang explained it wonderfully, you need workflows that enable cooperation and intelligent use of stored data. If there is more siloed data, or less siloed data, workflows will make it possible to address the issue. Behind this, you need collaboration tools in real time between professionals. Only then, when you've done all that do the enhanced intelligence machine learning tools come into play.

We're dealing with the design and then the use in real time integrated into workflows. We're dealing with a complex and slow process that will require reflection about organization, beyond simple information systems, and which, for you radiologists, will pose a problem in the way you work in collaboration with others. For hospitals, it is undoubtedly relatively easy. For independent radiologists who may work in different clinics, in different health care pathways, the question will be more complex.

In conclusion, if you're interested, there is a very good white paper on the state of enhanced intelligence on the AGFA HealthCare IT website.



W

II

François VORMS CANON

Deep learning, an inevitable development *

hile the talk is being prepared, thanks to the FNMR for giving us the chance to present the tracks and outlooks concerning artificial intelligence

within Canon Medical Systems Corporation.

Why is Canon involved in artificial intelligence in medical imaging? The Canon group just integrated the Toshiba medical imaging structure and is changing the functioning to accommodate the new issues. The change in the name from Toshiba Medical to Canon Medical has been in effect since January 4, 2018. So, this is the background history of our work in artificial intelligence, it isn't new.

This development was made possible through internal research, thanks to the acquisition of companies and knowledge, whether in post-processing or more upstream research. In the past, * Re-transcription of the talk 1 Computer Aided Diagnosis.

Canon has also invested in companies that work on diagnosis and contribute to big data access. It was mentioned this morning that the first experiments and steps towards artificial intelligence were the CAD 1 which are also available at Canon Medical among the post-treatment solutions. Today, we continue to make progress in the development of artificial intelligence with deep learning, which requires significant data processing resources and presupposes having partnerships with large companies such as Nvidia with which we just signed an agreement.

We are convinced that this development is inevitable. Why? Because, as you know, today there is no more medicine without imaging. We are witnessing an increase in the number of examinations because of the new indications as well as chronic diseases. Therefore, it is completely justified to think that artificial intelligence must be developed and it is our role as manufacturers. We are here to work with you, to assist you to simplify your daily life,

Chapter II - Deep Learning, an inevitable development - 141

to improve the workflow and think of patient security, Prof. Chang talked about that earlier.

Artificial intelligence will also make it possible to work with degraded images, with fewer X-rays still. Therefore, it is through artificial intelligence by means of big data that we will be able to quietly rebuild these images. With regard to this, a month and a half ago at the Yokohama conference, we presented our first CT scanner with on-board artificial intelligence, in other words, with deep learning and it will be available at the beginning of 2019.

I will now hand you over to my colleagues who know more about the technical aspect so that they can clarify the different tracks. Patrice will now take over.

Patrice COUDRAY (Canon)

Thanks François. Just to introduce my talk, we all know since this morning that there are several artificial intelligences, it isn't just one. There are several types. We all know that this will not happen overnight and that there will be several phases in the launch of artificial intelligence.

I would just like to demonstrate that in the first place, we don't have the choice because these large methods force us, the manufacturers,

and force you as well to support all these images, this huge data, with artificial intelligence solutions. The methods force us to develop AI technologies, for example, the first to arrive in our systems, DLR, deep learning reconstruction, which we will offer very soon.

So, why and what is the purpose of DLR? I'll take just two examples, CT and MRI.

Recently, we produced a new Precision CT scanner that provides performances that are two times higher than all the CT scanners on the market. This is a CT scanner that will provide resolutions like you have never seen: 50 pairs of lines per centimeter and the construction matrix in 1024 and 2048. It also has 0.25 millimeter sensors, twice as thin as the ones we've been making until now. Precision medicine and texture imaging are new notions that Precision enables us to develop in order to improve detection, characterization and prevention.

All these new notions are made possible by the thinness of the sensor. However, this new generation of sensors will require that the dose be decreased even more, which is one of the objectives of DLR.

Huge data should be supported and simplified. The new methods will generate, not hundreds but thousands of images at each examination.


We already have a solution available called AICE, which is the subject of our press release of April 10th. This solution is based on deep learning reconstruction. I won't go over this neural network learning technology which Prof. Chang explained very well this morning. It's like a black box, but it doesn't matter.

The basic idea is to obtain a signal/noise that is as pure as possible, the ideal signal-to-noise ratio. The Holy Grail is to acquire a pure, noiseless signal. Under these conditions, we can hope for images with extremely fine spatial resolutions, optimized temporal resolutions and acquisition times that are faster and faster. DLR will serve to get closer to the ideal signal-to-noise of 100%.

In addition, DLR, deep learning reconstruction, is much faster than any iterative technique available on the market today.

DLR can also be applied to MRI. In MRI, the software learns to identify noise in a specific way, isolate it and remove it from the image. That is new, because standard software removes almost everything, including useful signals. If you are coming to the ISMRM 2

conference in Paris 2 International Association For Magnetic Resonance in Medicine.

in two weeks, we will show you the first results of artificial intelligence in our images.

It is justified to require short acquisition times in MRI. This is an acquisition time of 1 minute 45 seconds. This image is acquired in high resolution, in 1024 matrix, but unfortunately, the more the matrix is increased the more noise there is. To limit the influence of noise, the user adds acquisitions, which markedly increases the acquisition time. The objective of DLR is to shorten the acquisition time and increase the signal-to-noise ratio.

We already have a research MRI in Bordeaux, France, that is equipped with DLR. The idea is to use 3T MRI images and to challenge the Image Quality of 7 Tesla.

I will now hand over to Cyril who will talk to you about post-processing.

Chapter II - Deep Learning, an inevitable development - 143

Cyril DI GRANDI (Canon)

Hello everyone. I work for Olea Medical and we develop advanced post-processing solutions in MRI and CT. This is what we're capable of doing today in deep learning. We segment and characterize CVAs, and all this is done automatically. We also provide classification and automatic segmentation of brain tumors and segmentation of knee cartilage.

I am a data processor, so, in a way it is my fault why we are discussing this today. I would like to reassure you that machine learning has been around for a very long time. I have been working with you for ten years and now deep learning makes it possible for me to say "great, we will be able to replace radiologists." In fact, this is not true at all. I would like to reassure you.

What we try to do in data processing is to provide you with tools to save time and enable you to work better. We can help you to improve the sorting of patients, to speed up case reading, and as we have all been saying since this morning, finally help you to save precious time on repetitive tasks for which there is no added value, to enable you to concentrate on your real job. And most of all, we want you to be more reproducible and more precise.

Thank you all. n


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Christophe LALA GE

The medical devices industry has a central role *

ello everyone. Thanks for the invitation. It's very nice to be here. I would just like to remind you of the role of industry in this artificial intelligence

circle of influence. This morning we talked a lot about GAFA 1, so, we are going to try to convince you that we, the medical devices industry, have a central role to play.

I will try to specify the strategy of the General Electric Group a bit, and specifically, the health care division. I will go over the pages you saw throughout the day very quickly. These are the conditions that determine the acceleration of artificial intelligence. As you said this morning and again this afternoon Doctor Chang, artificial intelligence has been around for twenty years, and there is a marketing phenomenon that,

at present, puts a stamp on artificial intelligence almost everywhere.

I will just try to explain what we are now trying to do. What does this page mean? It means this is the challenge for everyone, including industries. It means, obviously, there is an increase in costs. There is a need to control health care expenses. There is a deficit in health care professionals. It is obvious that we are at a time when we are searching for solutions to try to stay afloat, because, if we continue like this, with the high demand in mature countries, there will be an explosion. With regards to the problem of access, we know very well that in emerging countries, and we were reminded of this as well, there are almost six billion people on earth who suffer from a deficit in access to health care.

The last point, which I mentioned quickly, is quality: how can we contain expenses

* Re-transcription of the talk 1 Google, Apple, Facebook, Amazon.

Chapter II - The medical devices industry has a central role - 145

while trying to ensure and improve the quality of care. That is, in a way, what this page means. We think that, as if by definition, artificial intelligence denotes a breakthrough, all the conditions are in place to succeed. I'm going to talk about several spaces, but a lot has been said about the medical space in particular. The system space should also be covered. I already talked about it a bit. These are things that you know already: we are at the middle of the page. We are in the process of being propagated towards the predictive, and one day it will be the prescriptive. Very quickly, I'll skip this page, everyone knows about that.

Precision medicine was also mentioned. Now you have an idea of what this precision medicine means for our group in the three stages, which are: diagnosis, treatment and patient follow-up. At each of these stages, we think the best way to try to make a diagnosis, carry out treatment and personalized follow-up is, of course, to try to have this precision in the

procedures and in the diagnosis, and of course, even though data is accumulated crudely, we have the

means to try to find a pathway in the different episodes of the patient's health, that enable us to be prescriptive in the end, and maybe one day, to never have the disease set in. Strategically, we rely on two major cornerstones. The first cornerstone of artificial intelligence development is

infrastructure in the broad sense. It was mentioned today, this is what we do with our products, with our equipment and with our services. But this also affects the structures: we talked a bit about the hospital,

but there are savings pools to be made in the management of hospitals, and that rather concerns the process.

Next, it is about the way machines communicate and for example, how we can process the field of medical equipment, in the same way plane engines and planes are processed in general. In other words, for you this means you would never see a machine break down. We are rather into the Internet of things. These three spaces: organizational, products and the Internet of things, is the first area. I won't go back over this. It's about automatic quantifications, automated procedures,


all the softs that exist and which will be more and more efficient.

In the other area, which I called the patient care pathway, it is difficult to name it, but it is that area where big data is arriving. It is where the major stakeholders of information technology will quite likely want to position themselves. It's the patient's life as a whole, his episodes as a whole, how do I collect all the data to try, once again, to have the most standardized image possible and therefore be predictive.

Here, in this field, we have embryo products, programs, but there is an immense amount of work to do. And in this case, of course, data is no longer owned. It is shared by the entire industry, by all the different data acquisition segments. Here, the notion of clean data is critical, which means that we are not quite there yet, but we are getting there little by little.

In all our developments, we look at three targets: the patient, the system – I talked quickly about the health care system, I will give you an example to illustrate what I mean, – and of course the health care professional, doctors or paramedics.

I won't go over all that was said this morning, so, let us focus a bit more on the field of imaging now. To give you an idea of what is developed on our MRI, because MRI is a great time consumer with regards to creating protocols for the different parameters, it is a bit operator-dependent. At present, we're introducing software that makes it possible to look at a set of common platforms in our brand, or even the competing brands, how we make the connection between the size of the voxel, the parameters and the different intervening parties. We're going to register these cumulative data, look at them over time, and add a layer, obviously of machine learning, to this system. And in the end, according to the indication the machine will program all the protocols in one

click, and finally the same things goes for post-processing, and also in cardiac imaging.

You know with Arterys - YS Works, today we can go to work in the cloud, push data to the cloud, be predictive and in that way we are in the product. That exists, it will develop and it is relatively


easy, even if it is a bit like an independent operator, I'll get back to that.

For the other areas, automatic setting was mentioned. All those of you here who, like me, have been in this profession for a long time, but on the other side of the fence, how many times have you reworked segments, how many times have you stood back from the images. It's not quite over, and yet, it has been a long time. We, the imaging companies, and these are my words, are processing natives, and for us data processing is natural. Obviously, to succeed we must be able to find the very essence of a business model.

I'm sorry, I'm the dupe in the story, but financing was mentioned earlier. And even if we talk a lot about Amazon, Microsoft and GAFA in general, which invest substantially, I can assure you that they won't invest for long if the practice is not monetized, sorry about the Franglais. In other words, today, when we discuss with the regulator, he dreams of artificial intelligence for a simple reason. If he could eliminate the work of radiologists, for him, monetization is about zero. With such attitudes, financing will be an issue.

I think integration in an ecosystem is necessary and that is where we should work together. That is why I say the medical device, imaging companies and you, together it is up to us to build the future,

because we know what we are talking about. If not, there will be no financial resources, and we know very well that this value-based health care is the key. What is the impact for the patient? What is the impact for the health care system? If the impact is significant, the reimbursement will be significant. We will enter the modern world. Obviously, this is how we foresee the development of our products, which is critical. Because the quality of the data comes from the product all the same, and then the processing, taking into account everything that has been so brilliantly presented this morning. I am very happy to have participated in this discussion. Obviously, with the General Electric flag, it is a bit difficult to speak of France, but I will do it anyway. At present we have engineers based in Buc, you know, near to Versailles. We developed this little start-up that we took over in Strasbourg, which managed x-ray dosage. This is a very important entry point for the development of artificial intelligence. We created the GE Foundry.

As Doctor Chang reminded us earlier, the key source is the data scientist. A data scientist is the one who is capable of determining the indicators that will make it possible to obtain the expected results and which make the connection with data. There is nothing trivial about this. It is easy to say in one sentence, but it is very complicated. Thanks to our schools and our education in France, these data scientists have know-how that is recognized worldwide, and we


depend on it. I think it was important to specify that.

In conclusion, we will do nothing all by ourselves. Already, we do nothing all by ourselves. As I said, we work with Amazon on the cloud. Because, today, setting up a French cloud for our equipment is 500 million dollars at T time. I can assure you that expanding and maintaining a cloud, ensuring the security, just the air conditioning of a cloud you couldn't imagine… We can't fight that, but it's the cloud, it's the data host. We work with these people. We work with Apple on an IOS which provides a tool that enables deep learning on smartphones. Of course, we work with the major academic sites. We work with you. It's a joint effort, but it is the logical consequence of the digital revolution. What is at the heart of digital technology is that it is a mirror of what we all do. We must learn to talk to one another. We can only work in a consortium. What I'm about to say is not new: the development of scans and MRI, we did it with you. We make technology available for results and you tell us whether or not they are probative for the patient. This will continue with artificial intelligence. It exists today. It is in the process of accelerating, but this Brian Body story, I mean, everything I showed on my first page, the cloud or the algorithms, is indispensable. But what is also indispensable is knowledge in the field. Thank you.



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Antonin MARCAULT PHILIPS

Artificial Intelligence: a revolution built on three cornerstones *

would like to start by thanking the FNMR for their invitation to quickly present what Philips does in artificial intelligence. David Corcos, President of Philips France,

has also asked me to transmit his apologies as he was unable to join us today, but he would have loved to be here.

First, a few words to introduce Philips. As you must know, the group has decided to refocus 100% on health exclusively, by disinvesting in our other activities such as audiovisual, Hi-Fi and more recently, lighting.

As you know, the image is the core of Philips' DNA. We have been working in health care imaging for over a century with the will to constantly innovate. Moreover, this transformation of the * Re-transcription of the talk

group is symbolized by our investments in research and development in the field of images and particularly in the field of health care data processing and artificial intelligence. At present, Philips spends 10% of their revenue on R&D. That is approximately 1.8 billion in investments every year. 60% of this sum is dedicated to research and development for health care data processing and artificial intelligence. This means that we are a medical device company that is transforming and moving towards more software, more information technology, and more artificial intelligence.

In April, Philips decided to join the AI for humanity plan which was developed by President Macron and which defines the health care sector as a priority sector in the development of artificial intelligence in France. Mr. Chang, you repeated it earlier,

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Chapter II - Artificial intelligence: a revolution built on three cornerstones - 151

one of the advantages that France has is its clinicians, data scientists as well as data sources that are increasingly structured.

In the framework of this AI for humanity plan, the Philips group decided to establish a world artificial intelligence research expertise center in France based in Suresnes. The aim of this five-year investment plan by Philips is to build and co-build the next health care artificial intelligence algorithms, mainly in medical imagery and genomics, with you and all the French and international stakeholders.

As you saw in the previous talks, imaging is becoming more and more enriched with biological and genomic data. This is called radiomics. It made sense for us to localize the best French experts in France on these subjects within a world AI expertise center.

The third dimension of this world artificial intelligence expertise center is the ecosystem. It seemed indispensable for us that this center collaborate with the best stakeholders in the AI and health care ecosystem in France, regardless of whether they are the hospital institutions, private offices, public institutes or SMEs and start-ups. Through this center, Philips is committed to investing in supporting the most promising health care French Tech projects. We can see that there is significant

potential and many young talents in France. In July we launched an investment fund of €50 M along with CapDecisif Management and BPI France to invest in the best e-health, health care and artificial intelligence projects. Moreover, maybe some of you are members of scientific boards, or are even start-up founders.

To answer the main question of this conference, before being a revolution, artificial intelligence is a chance: for us, healthcare professionals, for us and for the patients.

It's a revolution built on three cornerstones. The first cornerstone is a technological and clinical chance. As we have seen, many algorithms make it possible to simplify the workflow for radiologists, enabling better display and characterization of images. I strongly believe that the strength of medical device manufacturers is to be there with their equipment in the front line at your side to collect data. We must also be at your side to exploit it. This is the meaning of this world artificial intelligence expertise center. It is also the meaning of all our developments, both on our equipment and on our software.

You must remember that artificial intelligence is not new. It isn't new at Philips. It isn't new in the world. We talked about deep learning earlier, deep learning was created in


1965. The first neural networks were in 1943. The great novelty today is that we have tools to exploit these data: calculating power, storage, more and more structured and bigger data.

The second chance, the second cornerstone is the organizational chance. Artificial intelligence is a wonderful opportunity to improve the efficiency of offices whether it concerns equipment, patient flow or internal organization. At Philips, we developed several tools, notably PerformanceBridge, which makes it possible to monitor the performance of a technical support center as well as the patient and operator flow in order to identify bottlenecks and optimize flows. These tools are more and more predictive, which means they can anticipate the need for operators throughout the weeks, for example.

The third cornerstone is an economic chance. The current system is moving towards a health care system which is based on value, in other words, value-based care. We are moving towards more and more efficiency with tools such as Illumeo that facilitate the workflow and adapt to the 1 Union Nationale des Caisses d’Assurance Maladie (French Health Insurance Fund Authority)

clinical context of the patient and the health care professional. At this stage, I would like to thank Professor Chang who presented you with the details of the Illumeo solution which was co-developed with Philips, and which is now integrated in the radiology work environment. This is practical artificial intelligence that is accessible today and facilitates the daily lives of radiologists. This ensures an improved clinical context for the patient, and also makes it possible to prioritize the workflow and the work lists and thereby prioritize the most urgent cases.

With regard to this, I would also like to mention the agreement that was concluded between the FNMR and UNCAM 1 on the

pertinence of care. It is also a chance for the health care system and a first step towards a quality-based system. When we talk about private practice radiologists,

we're also talking about entrepreneurs. I talked before about an open ecosystem to ensure the emergence of innovation to serve patients and health care professionals. To enable the emergence of the co-creation of artificial intelligence projects, it is essential that each stakeholder equip himself with the latest tools. At present, most artificial intelligence tools are open source tools accessible to each of you, each of us,

Chapter II - Artificial intelligence: a revolution built on three cornerstones - 153

if we have the capacity to exploit them.

Philips decided to create a joint artificial intelligence platform to gather the best tools and databases available for health care innovation. We verticalized these tools in health care. We made them available on a platform called HealthSuite In-sights, which we presented at Paris Healthcare Week this week. It is meant to be a co-creation platform between data scientists. They might be interns at Philips, other manufacturers, or in start-ups; the clinicians that you represent, whether they are in private or public health practice. These tools are available on this platform so that we can co-create the algorithms that will serve your clinical practice tomorrow.

If we want to create pertinent artificial intelligence tools, we must work on a common platform where they can be developed and distributed. To summarize this platform, I would really like to make an analogy with the Apple App store.

As a final word, I would like to take advantage of this assembly to present an international acceleration program called AI for radiology in which each one of your start-up creators can enroll. It is structured in such a way that you can work with the best Philips experts

to accelerate your solutions and access all the clinical and computer expertise from Philips and our external intervening parties around the world.

In conclusion, artificial intelligence is a chance for all of you, for all of us. It cannot be done if everyone stays in their corner. It is essential to work in an open ecosystem. We must create together, but in order to do this, we need tools and platforms that are available to everyone. This is Philips' commitment to make clinical radiology AI progress in the service of technologically advanced radiology. Thanks for your attention and I will be available for your questions.


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Jean-Baptiste LEPRINCE SIEMENS HEALTHINEERS

AI, an opportunity for a preventive model at a reasonable cost *

hanks for your invitation. My name is Jean-Baptiste Leprince, and I am the Services Director at Siemens Healthineers. It is difficult to

speak at the end of the day like this. Excuse me in advance, if you have the impression that I'm belaboring the point.

Artificial intelligence was mentioned and I won't go back over the definition. I will try to present our point of view, in any case the impact on radiology in particular.

To begin, if I should scan the context just a bit, two elements come to mind:

1) The first is that for over one hundred years radiology, by the very essence, * Re-transcription of the talk

was founded on the basis of scientific discoveries. Of course, I'm thinking of x-rays. And it hasn't ceased to adapt to changes and scientific inventions. The most significant that comes to mind is, of course, the discovery of nuclear magnetic resonance and its applications to imaging which were produced at the beginning of the 1970s.

2) The second point, and this was discussed at length today, is that artificial intelligence is not new. Besides the theoretical concepts, I myself remember as a young student in the 1990s at the École Polytechnique, I had friends who completed neural network programing projects. Since 1996, at Siemens Healthineers we have been conducting machine learning application research and development. We now have over 400 patents. Deep learning, which is

Chapter II - Artificial intelligence, an opportunity for a preventive model at a reasonable cost - 155

more recent, has been developing since 2012, and we have submitted over 75 patents to date.

Therefore, the question in light of these elements is not really to know whether it is here or not here. Rather, it is, what type of transformation can intelligence provide today? In that case, two major questions arise: breakthrough or profound change?

If we look at the breakthrough aspect, a subject that the press has taken over, it is delicate, because it is accompanied by millenarian myths and fantasies. I believe, we are rather entering what Michel Serres calls the third major anthropological revolution of humanity, after the invention of writing and printing: the digital revolution. The field is very far-reaching. It is often represented through iPhones which, in 2007, profoundly changed our usage and our lives. This angle can rapidly take us into a philosophical debate about the end of work.

It fits in with the words of Doctor Chang which is that radiology, since that is what I’m talking about at any rate, is an aspect

that occurs after many other aspects of revolution and change in our lives. So, I won't go any further into the breakthrough aspect.

Our vision is rather that AI will profoundly change radiology. Up to what point? It is a real subject of debate, but it will help radiology a lot and radiology will adapt as it was able to do with the many innovations

and inventions it has experienced. So, by looking from the perspective of profound change, different levels can be distinguished.

The first level

has been extensively discussed today: the adaptation of the competences and practices of new generations. Education, in particular, is important. In and of itself it isn't simple, because along with the digital revolution, it will be accompanied by generational challenges. After generations X and Y, Kofi Annan 1 talked about a generation of inheritors without inheritance; faced with people who will have new professional and personal aspirations. These are important subjects and are a part of the challenges of artificial intelligence in radiology.

1 General Secretary of the United Nations (1997 - 2006)


Then, there is a second level that is more macroscopic that I will try to describe in the few minutes I have left.

I will start with an obvious conviction, or in any case, a common sense conviction. Today when we look at the cost structure of our health care system, the needs in volume that go with it, and the vision we have of this Grail of preventive medicine, the difference in volume is such that it is inconceivable to have the cost structure of the curative model of today to practice prevention.

The immense opportunity that opens up with artificial intelligence is the capacity to return to a preventive model with a cost structure that is reasonable for everyone.

This brings us to issues of public health which will be accompanied by many opportunities because we are talking about new services that need structures: • The first, as we have seen, is access to

qualified data. It is truly a major technical, technological and legal subject.

• The second consists of clarifying the entire legal debate about liability. It was mentioned today. Our vision is that it is actually a complex subject that will require a major discussion within our society.

• And the third is the creation of an environment of associated codifications,

to recognize the value that is provided by these services.

That is our vision which I wanted to present to you in a few words.

For us, this is an opportunity. It requires a lot of work, the completion of many projects and the establishment of an institutional framework, but it is a health care issue.

Fundamentally, we are experiencing a great opportunity for radiology.

Thank you very much.

Chapter II - Artificial intelligence, an opportunity for a preventive model at a reasonable cost - 157

III - Discussions


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- III DISCUSSIONS

Open debate

hroughout the day, there were discussions between the speakers and the participants in the conference. We summarized 1 this debate and presented the integrality of Prof. Paul

Chang's talks.

Jean-Philippe MASSON I had prepared a question for Mr. Besnier who explained that AI might cause the doctor-patient relationship to deteriorate. I realized that in fact, the successive speakers who came after him answered by saying, "No, not at all. On the contrary, AI will give the practitioner more time to be close to his patient." I think this is something we've been defending for a long time. The various talks demonstrate that it is complicated but that, first and foremost, any AI project that is implemented must have a strong ethical basis to avoid certain deviations.

Question: Are data and data integration more important than algorithm?

Paul CHANG I will try to extrapolate the question. You're absolutely right. If I understand the question correctly, you're saying that, as I mentioned, the fundamental paradigm change from machine learning to deep learning is data driven. The algorithm itself is agnostic initially and alters, as with any statistical method, the parameters. Or the hyperplane prediction is based on the data. You are absolutely right, one of the problems with that approach is that the data that feeds it has to be annotated and interpreted. That is one of the biggest challenges to the application of deep learning today in radiology. A good example of that is:

1 Under the responsibility of the editorial staff

Chapter III - Discussions - Open debate - 161

a few years ago IBM spent USD 1 billion to acquire a small Chicago-based PACS company, primarily because they wanted to feed Watson and its deep learning initiatives with vetted interpreted data to train and validate the systems. Unfortunately, they found that the majority of clinical archives weren't annotated. We store images, but we don't correlate them with outcomes, with truth. That is the big challenge. There are some clever approaches where Artificial Intelligence could be used as a way of extracting truth from archives, but we will get into that later. If I understand your question correctly, you have to bootstrap these machine learning algorithms with vetted or interpreted data and this is one of the Catch 22 situations, one of the main challenges in adopting these technologies, because most of our datasets are not vetted, because we leave the humans to do the interpretation and they leave no precise interpretation trails. Most of the truths we have in medical systems are buried under "non-narratives" like our critical paths, which are very difficult to extract digitally.

Question: It isn't that artificial intelligence makes people anxious, but rather humans. In the end, what does the patient want? Aren't we in a society where we want to be reassured? Don't we want medicine that is completely controlled

by artificial intelligence in which ethical apprenticeship or dialogue with the doctor becomes secondary?

Jean-Michel BESNIER Yes. I think the right question is: what does the patient really want? I think the patient is like all of us, always torn between antinomic behaviors. When he's in a distressing situation he agrees to be considered as a simple organism handed over to medicine he hopes will be more and more technical. He knows that dependability is more and more related to machines. So, he is ready to let himself become basic, nothing more than that organism. On the other hand, he would really like what is irreducible in him in relation to the machine and in relation to the animal to be taken into account. He is a being endowed with a symbolic function. In the first place, his symbolic function is related to the fact that he is a talking being whom language allows to say what is not, to say the future, say the past, but he is an historical being. He's a being with intelligence, which in general, enables him to resist automatisms. I am astonished to see that this acceptance of the concept of intelligence has been completely whisked away today. Intelligence is first and foremost the possibility to fight against instincts and against the automatism of instincts. A human being is a being who has a vocation to a certain uselessness and disinterestedness, etc. This is the component of the human being,


a being of symbolical dimension, the human being, a being based on the animal and the machine. If medicine lets itself become more and more absorbed by artificial intelligence – it does it and it is normal because it wants efficiency – it runs the risk of losing this dimension of meaning which is nevertheless, the patient's aspiration. Concretely, your patients are more and more appalled to see that they are less and less in a relationship where they can have a discussion with you. First of all, screens and images come between you and them. The result is that in general, the average patient leaves his doctor's office saying, "He didn't even look at me. He only looked at his screens. He only looked at his images. He only looked at my tests, but he didn't even talk to me." It's as simple as that, but I believe that it must be taken into account. If, in addition, the patient discovers that maybe the doctor is more and more deprived of initiative by machines that, first and foremost, collect data and from all over the place, it makes him panic.

Question: There is much talk about ethics. It is really becoming a cliché. Mr. Chang said that we all have a tendency to take possession of new things rapidly, but take a certain amount of time to assimilate them. From an ethical standpoint, is societal ethics justified, for example, we took ten years to accept digital mammography in screening, when we knew perfectly well from the start that it was a really disruptive technique and that for these ten years

that we wasted, patients lost an opportunity? Can't we speed up the discussion on the introduction of these new techniques and also provide the means because we know for example, that in the new digital proposals, the liberal professions are completely excluded?

David GRUSON Our primary conviction is that the main ethical problem is the absence or insufficient amount of use of digital data and of data sharing. Once that is laid out on the table and we are able to make people admit it, we will see what position the National Board of Ethics takes and most of all, what position the public authorities take concerning the future bioethics law. When we can make people admit that, and make that acknowledgement, that will be the real breakthrough in the way in which innovation is created and supported. When we look at things and put aside the fantasies a bit, ethical risks, risks of delegating consent to AI, risks of minimizing the consideration for the individual situation in terms of a logic that is too collectivist in relation to algorithm, these risks are identifiable and certainly partially controllable. We're not starting from nothing. As the Board reminds us, there are professional codes of ethics that you apply every day in your medical practice and legal rules on professional liability that are applicable in quite a number of cases. Research efforts must be supported, not


on insubstantial or abstract ethics, but very operational ethics with means of regulation to support the roll-out of new technologies.

Question: We're under the impression that there isn't just one artificial intelligence, but many. It all depends on where you put the cursor. Does it simply concern the sophistication of existing tools or is it really a technological breakthrough? It has been said that these tools require financing. Who does the financing and why? The rate of development of all these technologies won't be the same between countries in the North and those in the South. Doesn't this run the risk of generating conflicts?

Cédi KOUMAKO I'm going to talk about the north/south inequalities. I am a bit concerned, as you can see. As you said, there are several artificial intelligences. There are different ways of looking at things. One way is to say, these will be rudimentary tasks, so that radiologists or other doctors do everything that is complex, and AI only takes care of rudimentary tasks. Another way of looking at it is to say, finally, we have a machine that is capable of receiving a large quantity of data and of processing them. We will entrust it with complex tasks, try to group together the rarest cases in the world and entrust this machine with complex tasks so that it can identify these complex tasks.

In this sense, when you talk about north-south inequalities, this can be positive because north-south inequalities exist now. There aren't a tremendous amount of countries in Africa and South America that have access to expert radiology or radiology experts. Being able to provide these people with radiology expertise through artificial intelligence, for me is a good thing. We could even provide them with radiology expertise from Europe thanks to radiologists who are relieved of their usual rudimentary activities, and that is rather good. It is up to us to ensure that our concept of AI helps to reduce these inequalities. That is my vision of things.

Florent PARMENTIER To continue along the lines of what was just said, several artificial intelligences, yes, I won't go over it again, but most of all, several potentials, several ways to envisage the use of artificial intelligence, and even to finance it. What is funny is that in this study by Roland Berger, if we compare these 3,000 start-ups that use AI as an essential point, we see that there are European preferences. On a worldwide basis, 6% focus on questions of security, 3% in Europe. If we look at memory, health care and biotech, on a world level it is 9% and 8% in Europe. This means that the Europeans must also rethink their positioning. What dominates in the European part is 35%,


I believe, of consumption which is B to B 2, while on a world-wide level it is 25%. These are just some figures to show you that the concept, use and way of envisaging things, not only are there several artificial intelligences, but there are also several ways of understanding and positioning in relation to artificial intelligence.

The second point on "who finances and why" is an essential question. There is a special feature and a Foreword. In 2016, through various venture capitals and their major companies, Americans financed the equivalent of 23 or 26 billion dollars in artificial intelligence research. European financing was rather to tune of 4 billion dollars, and therein lies the difficulty. That means that not only are Europeans split up, that is the first problem, but in addition, they have no ecosystem that enables them build the next step.

Question: In his talk, Mr. Soulier mentioned Article 11.12 of the Public Health Code, when he talked about radiologists' obligations. It seems to me that this article only applies to the hospitalized patient's medical record.

Laure SOULIER With regards to obligations, I targeted the provisions of the Public Health Code that define the medical record and that are aimed at communication and free communication. If the patient asks you to transmit 2 Business to Business.

his record, you must access it.

Jean-François MEDER Two short questions for Mr. Soulier. The first is that I thought records belonged to no-one, but that the patient owned their records for medical use. In this respect, is use of data for educational purposes a medical activity or not? The second is, is there a legal definition that doesn't exist in imaging, which distinguishes anonymization from dis-identification?

Laure SOULIER I don't have the answer to the second question. There is a keyword, anonymization. Now, I'll go back to holding out a hand, or rather taking someone's hand. That is because you have outright fears now. What is the patient afraid of? The thing is that even with anonymization, today if you put two start-ups face-to-face, despite anonymous data the person to whom the x-ray belongs can be found. Therein lies the difficulty today. That is why I say that to bypass this problem, which I think is very difficult to solve, let's find a different approach to the problem, let's take a different path. The way I see it, we must be able to have the patient as a privileged partner so that we can have the authorizations and consent and be able to use this information with their agreement for research and educational purposes in order to advance.


You see, I'm looking for another angle, I'm only committing myself.

Question: A question on consent. We know that various institutions such as hospitals sell their data at present. In the framework of the GDPR, is this search for consent absolutely necessary and enforceable? In the framework of the GDPR 3

can a hospital still sell data to GAFA 4

without the patient's informed consent?

Laure SOULIER Today, what is certain is that there must be consent. It must be sought and it must be obtained. At present, there are always additional steps. Today, what is criticized, whether it is the hospital or the Apple Watch or anything else for example, consent is biased - it is the ethical problem - it's a bit forced. In other words, you will inevitably agree so that you can use something, and that is where the debate is not completely clear. We will be in an evolution, we are in this evolution. What will be the form of consent to obtain? We must all work on it together, because today you are told about consent. That is good. Websites have changed their 3 General Data Protection Rules 4 Google, Apple, Facebook, Amazon.

policies, you've received 20,000 e-mails, … I defend doctors, as you can see, but for me, you must also defend the patient. The agreement is a bit forced. He doesn't have the information. The consent is there, but it is important that he is informed about the purposes. And that means compliance with the entire GDPR. It means having more targeted information.

Cédi KOUMAKO Afterwards, I noticed that my talk earlier on the opinion or position of the residents was probably a bit pessimistic. But what strikes me about this survey we did is that young residents are not totally informed. And disinformation is scary. There are a few answers in this survey that make me think that things are rather positive. They want to be informed. They are not completely resistant to the idea of artificial intelligence. I would like to point


out that the answers that might appear to be negative are very connected to a lack of information. There is an on-going research with the CERF (French Radiology Teachers College), which has included an artificial intelligence training day in the new training modules for residents. We have a meeting with the CERF soon to try to organize AI training for residents because it is essential.

Question: Artificial intelligence will be quite useful to our radiology practice. At present, we have the equipment (MRI, etc.) that enables us to practice. But artificial intelligence will open up the possibility to other specialists to practice by themselves without our knowledge. How do we protect radiologists? Is it by maintaining ownership of our equipment?

Robert LAVAYSSIERE There is a first answer, which is the example of the PACS and image distribution. Whether it is at the hospital or in a large private institution, clinicians always come back to see us to ask for explanations concerning images. We have a role as imaging experts. It is a recognized role that we must keep. In fact, Jean-François Meder referred to image factories or imaging factories, that is a dangerous deviation. But once we keep a medical role, and also in relation to the patient,

there is no reason for us to be "deprived" of this role of expert. The second answer is that the other specialists are also in quite an unfavorable demographic situation. They sometimes have other things to do. Certain specialists like oncologists, orthopedists and pneumologists look at images, but this doesn't mean that they want to do imaging themselves. I'm talking of the French system. It is true that in the American system, it's different, because we know the burden of self-prescription. But this follows other rules besides the economic rules that we know. I think the danger in France is relatively low. As long as we remain in the field and highlight the quality, we won't have this current problem of competition. It is true that now, in the distribution of images to others, notably, to general practitioners, questions come up that cause us to waste time. It happened to me again not long ago. A GP who had looked at MRI angiography images on native cross sections, was asking me questions that were not pertinent. But we have to accept that. For the question of ownership, not just anyone will have an activity authorization. Next, I don't think artificial intelligence as we understand it today will replace radiologists any time soon. That's the first thing. On the other hand, we are already partially using artificial intelligence


and a certain number of techniques. That doesn't mean the other specialists have gotten hold of them.

Patrick GASSER 5

I'm a gastroenterologist. Could you do just a little more long-term planning over ten years? We actually have artificial intelligence in our offices, in our health care institutions. It will be a lot simpler to have in at least five or ten years. Shouldn't we be more versatile in the care of our patients? In other words, work together from the start of care. And I will go even further. Shouldn't we create health care, goods and service companies in the future, by creating partnerships with radiologists and specialists, especially in the same company? I'm talking about the private institution of the future, in the framework of the territory and the health care answer for everyone.

Jean-François MEDER The structure you are talking about is the hospital.

Patrick GASSER It is absolutely not the hospital.

Jean-François MEDER I am perfectly comfortable talking about it, because we are together from the beginning to the end of care, including in the follow-up care…

5 President of UMESPE

Robert LAVAYSSIERE I kind of wanted to give the same answer as Jean-François. With regards to private practice, I think data collection is what an improvement in functioning through artificial intelligence can provide us with. And we are in fact, in need of that because in private practice one of the major problems is the pertinence of the request. I don't dare say the prescription, maybe not for gastroenterologists, and even then. In a large number of cases, there is little precision or none at all regarding the purpose of the examination itself. The patient doesn't always know, it's a problem we all know. Actually, you might think that a pertinent request for care would end in a pertinent response. In that case closer collaboration could actually be important.

Paul CHANG That is a good question. I get asked that a lot, so I prepared an answer. I think one of the things you'll find is that the adoption of Artificial Intelligence is going to be no different from the adoption of any potentially revolutionary technology in medicine. I'll try to answer your question along with the other gentleman's question about whether AI was going to replace us and should we protect our CT scanners, etc. I admit that it made me smile, because we had the same conversation in the early days of PACS.


Back then, everyone said if the images were digitally available, there would be no more radiologists. People were anxious (and vociferous in their concern), because they thought that PACS would completely deprecate and destroy the practice of radiology. The opposite happened, and radiology has continuously grown. Unfortunately, that is the problem. Right now, radiology is in its adolescence and as a result of its growth, the expectations of our clinicians and patients have increased to a point where we can't handle it. This is one of the problems with Artificial Intelligence. You wanted a picture of how things will be. I thing there will be three phases. First of all, we've had Artificial Intelligence for decades. For those who have used computers in diagnosis or mammography, you've had that for 30 years. When people ask when it is going to happen, the answer is that it already has. Artificial Intelligence is already here. You'll witness the very first phases of deep learning systems very soon, but you won't even notice it. The very first implementation of Artificial Intelligence when it comes to deep learning will be the reconstruction of MRI and CT. They will be embedded systems, which GE, Siemens and Philips are already doing. The benefit of this is that you will be able to do reconstructions that would dramatically reduce the cost and time spent to acquire MRI images. Exposure of our patients to radiation

will be significantly reduced. One of the big low-hanging-fruit applications of deep learning, you will see within the year if you upgrade your CT or MRI scanners, will be the application of deep learning in acquisitions. It will be embedded, and you won't even notice, other than the fact that your radiation doses will suddenly decrease and MRI throughput times will increase. You'll see that first part. The next phase you'll see is exploiting what other industries have already done with Artificial Intelligence, which is eliminating all the repetitive tasks, all the "trivialities" that we have to do for our patients. You will see significant improvements and, in that case, the simplest applications (low-hanging-fruits) are those that I call "minimally heuristic" deep learning applications. For example, the improvement in protocols; I don't know how it is in France, but in the US, we have a dichotomy of service where certain practices spend a lot of time improving the quality of cases protocols. That is very important, but it is very difficult to do because you need clinical context for protocols, which means it takes time and you need access to clinical information which is very frequently absent. I have already seen this in start-ups, applications and with big vendors for protocoling, managing, and sorting cases, using Artificial Intelligence to facilitate the search for clinical context


and communication with referring physicians to understand exactly what they want. There is a time-lag now. We often waste too much time trying to understand what you really want, or we just do a generic exam on portal vein circulation and don't worry about the rest. That's not good at all. There will be another phase where the deep learning applications will not really be at the core of radiology, but rather in all the peripheral work such as protocoling. Your worklist will suddenly become very intelligent and will actually be the next phase because those are the things for which it is easier to get regulation authority approval. So, the next phase you'll see concerns the worklist, which is quite basic now. On your current PACS, your worklist basically comprises stat, ER, etc. You'll see worklists that say you should look at a case from ER first, because there is an anomaly, we see some life-threatening condition. You'll see this in one to two years. This is what I expect to happen. It has already happened, it's in the pipeline and people are going to buy it or you will get it for free. The next phase that radiologist will have to face will be very interesting. Some tasks are very time-consuming, like the examples I showed you of trying to find lesions in MRI,

multispectral datasets, trying to resist measurements or modified resist, or time-activity-type perfusion studies in the brain. These tasks will also be automated for you. You won't be entirely replaced but you will save time. Many studies show that radiologists spend about 60% of their time doing repetitive tasks, not actually using their brains. We do a lot of stuff like rearranging images to try to use our brains. You'll see that low-hanging-fruit within the next few years. The next change you'll see very soon is not the use of Artificial Intelligence in imaging, the convolutional neural network, but the extraction of natural language processing, like in other industries. The big boys in many industries, the Googles, the Amazons, use natural language processing to extract consumer perception from social media. Next year in particular, you'll see many major businesses using this technology to extract meaning and outcomes from unstructured reports, pathologies, clinical notes, etc. Too often, we don't understand enough of the clinical context to interpret the images correctly, because, as you know, we are only imperfect observers. Artificial Intelligence is an imperfect observer as well and as a result, Bayes Theorem applies, and you need the prior conditional probability in order to interpret


the image correctly. That prior probability will be delivered by an AI with the ability to say that it just recognized an abnormal lab value and that the patient has a constellation of symptoms that the clinician did not mention in the prescription or instructions, but we know that the patient has abdominal pain, leukocytosis, etc. AI will let you know that you should take all this into consideration, and you'll probably see that. I know my answer is getting too long, but what I'm saying is that the implementation of AI will be so gradual that by the time it is really here, we will be ready to use it. Everything else will be so dependent on it that when radiologists are really faced with it, whereas now you have to make decisions about this subject, then you will use AI as a real partner. The way I see it, AI right now is like a very enthusiastic resident, a puppy that wants to help you and do all the work for you. But, by the time it becomes a real partner it will be ubiquitous, and we'll be so used to it that we'll be able to use it appropriately. I'm not worried about this at all. We keep saying we'll be replaced. We said it about PACS and many other things. I think the use of AI will be gradual. Because of the constraints we have it's going to take longer. This will make it easier to adopt AI in comparison to what happened with PACS. I think PACS was much more revolutionary than AI.

From the floor So, we'll be addicts.

Paul CHANG Exactly Applauses

Question: How long do you think it will take to adapt baseline medical studies to everything that is happening, and to the fact that we won't have to accumulate all the knowledge we've been able to acquire, considering all the possibilities to work differently that we'll have?

Paul CHANG Blame him. It is not my fault you have to wear these things again. I think the question you asked was what is the situation in the US? I'll tell you this much. We love to brag that we're so much ahead, and I found it very interesting to hear you say that the US is in a dominant position. But we're not, health care professionals are gentle, and in Israel and China they are much more advanced. We have many constraints, which are very similar to yours. We have HIPAA 6, which is very similar to your GDPR. You are absolutely right. We need vetted data to feed deep learning. As I mentioned this morning, deep learning systems are the brute force of machine learning.

6 Health Insurance Portability and Accountability Act – Law enacted in 1996 concerning health care and health insurance in the United States.


We need vetted, interpreted data. Our data in the US are very primitive and you have a fundamental advantage that you're not taking advantage of. Earlier today, a speaker talked about the various visions of the future of Europe in this field. I wanted to step-up and say that you're not leveraging your biggest strengths. One of the biggest disadvantages we have in the US is our healthcare delivery system and data infrastructure is fragmented, we don't have a universal identifier. An entire system can't be trained correctly from just one hospital. You need massive amounts of data, especially if it concerns an extremely rare cancer. That is why most of the deep learning applications you see at the RSNA and in the US are so stupid, so unintelligent, and so rudimentary. Lord knows, we don't need another bone age algorithm. There are about one hundred bone age algorithms and there is a reason for that. It isn't because in the US we are so perplexed about bone age in pediatric patients, it's because the use cases are driven by available data. I can generate a random number of cases to train a deep learning system from bone age, because the image itself is the same age as the patient. The problem is that we are just as far behind. In fact we are further behind than Europe when it comes to available data. I don't want to go into the details, because that's a whole other talk. In fact, after this conference, I'm going to Amsterdam to talk to their health authority

about image exchange, etc. Your advantage is that you could have a unique identifier. You could create archives that vet data with this identifier. We are hopelessly behind in that. Our advantage is we can get investments. We can make a good sales pitch. At the end of the day the implementation always takes much longer, but we have the capital and the sales pitch. Our strategy in the US, which has been very successful, is based on fear, uncertainty, and doubt. We make our sales pitch and boast about our expertise and then we form partnerships with the Chinese. That's what we do. That was your third model, the collaborative model. That is why I thought your debate this morning was so important. I've been to many conferences in the US and I've spoken about AI, but this is the first time I've heard ethics actually being discussed. Shame on us, because we don't talk about ethics, or maybe we're "hipper" and our ethics only apply to Americans or white people, and not people of color. I think that as a person of color, I could annoy people here and the interpreters might wonder how to interpret what I say. What I mean is that in the US, we have the capital and then we go to China where they have loose laws about protecting patient data, and we get massive amounts of data. That is the collaborative model that is in fashion now. We


go to countries where the ethics are looser, not well-defined, or we don't discuss them explicitly. It sounds a lot like the old days when we would drill for oil in certain countries without informing the local population, and exploiting arbitration. We're doing the same thing with intellectual property. We're exploiting the fact that systems, protections, codes of ethics are not as well established as in our country, and we exploit the data that is available locally, etc. I apologize for dragging on, but you have a huge advantage here. You have an ethical framework. You have a personal data protection law that is more flexible than ours. I've read your law and you explicitly mention "tokenization". Your laws address anonymization, etc., but that is a dead end because anonymization is useless in deep learning which requires a correlation with outcomes. Therefore, you can't anonymize data, but you can "tokenize". I know that there hasn't been any discussion about that, but you should read your own standard on tokenization, because it is a much more sophisticated approach. With tokenization and your ethical model, you could be leaders in providing data in an ethical framework. Just, do it. As I said before, there is nothing new about deep learning, but that's not entirely true. Yes, the big enabler was graphic processing capability, 7 Gross National Product – Produit National Brut

which I mentioned this morning, but I fibbed a bit. Some of the advances that made deep learning possible came from this country, from French scientists and mathematicians, but they aren't here anymore, they're in California or China. That shows that you have a problem, and that you need a program like Israel’s TALPIOT, which I prefer to DARPA, because it has a significant effect on their GNP. 7 There are policies you can implement to prevent the human capital flight, by leveraging your very intelligently structured data protection laws, especially tokenization, to speed things up in an ethical way with patients as partners. In the US, we don' trust our patients, so we try to bypass them by going all the way to China, if necessary. I think you have an opportunity with an intelligent population to engage patients as partners and to assume a position of leadership. It wouldn't only help you to get results, but would position you as well. I agree with Putin, this is of strategic importance for Europe. You must become leaders in this field, not just for health care but for other applications. You're not taking full advantage of your position and I know that sounds horribly patronizing coming from an outsider. However, from what I see and read, you have wonderful advantages that you woefully underexploit, when you could


become real experts. I apologize for that. Applauses

Sorry. I apologize. Blame him now. The question he asked was did we have any special training? There's a wonderful survey that you talked about, about services rendered to physicians and training in this field. The question was if we had any advantage in the States? I can give you the official answer as a representative of the RSNA 8, which is of course yes, we do. Do you want the truth? No. In fact, I just reviewed a paper for the journal Radiology that will be accepted. It's about two surveys very similar to yours, one from a Canadian radiology resident and the second that the ACR 9 did for the American radiology residents. The results were exactly the same, they were scared, and they knew nothing on the subject. The take-home messages are exactly the same as yours and that is that we are totally ignorant about this technology and we should be less ignorant, because through ignorance we'll make bad decisions. Are we in a better position to educate our trainers? Not really. I would say we have one minor advantage, which is that it is now a requirement in US programs, to educate all radiology residents in training in information technology. There is a formal information technology curriculum that our residents have to follow. Therefore, we do have the potential 8 Radiological Society of North America 9 American College of Radiology

to include Artificial Intelligence in this program, but the problem is time, because there are other things you need to learn to take care of your patients. I was just on a committee that allotted one lecture to Artificial Intelligence, basically the one I just gave you. That is not enough for our residents. We're really working on it, especially the ACR, but we absolutely need to improve training for our residents, because there will be an impact. When AI is really established, we will all be retired, but before it gets to the point where it is really a threat for us, two things will happen. Many of us will be retired or dead, and by the time it has a real impact on radiology, that will be the least of society’s concerns, because the impact will be much higher in other fields. Before it replaces radiologists, it will replace bankers, managerial staff, administrators, and all white-collar workers. Countries will have 40% - 50% unemployment, if AI ever gets to the level where it is a cause for concern. I am more concerned about your generation. Our generation will get the benefits. We won't have to measure resistance or call people anymore. I am not talking about singularity, which will never happen, but when AI gets to the point where it can completely replace us, that will be the least of society’s worries. That will affect your children and that is why we need to train you.


I'm not worried about having to train you, because all you have to do is just practice medicine and suddenly the next revision of your PACS will suddenly be easier to work with. I don't have to train you, because the sophistication of these systems will not be threatening enough, it will just be helpful. I don't have to train anyone to use a smartphone, it's not scary, it's just nice and cute, and my photographs are now organized in a better way. It's the smartphone generation that will really have to make the tough decisions, along with the administrators, lawmakers, and politicians of that generation. There will be cause for concern when this technology develops in such a way that AI becomes a real partner. This is where the requirement for education comes in. We need to train the youngest ones, while the older ones reap the benefits of this slowly maturing technology. They are the generation who has to make decisions. Applauses

Question: Jean-François Meder asked a question about the number of radiologists. Can he be more specific?

Jean-François MEDER I didn't mean to say there are too many radiologists. What counts in this plan is the question, not the number 8,500. Today, we base our judgement on the fact that there are medical deserts, and that there are difficulties related to the 10 BA, Masters Doctorate.

fact that a certain practice in certain structures might not be attractive. But do we have to? At present, we protect work interests for 40 years. That is what residency will mean. But can we say that the 8,500 radiologists are adapted to work that will change through artificial intelligence, and work for which the scope could be modified by the creation of super operators as a result of the LMD 10? I don't know. Nobody is answering this question. We're going to train, but I simply hope that the level of training for residents is adequate so that they can get onto a different course. That is the only advice I can give. n



Round table discussion *

Dr. Alain Houpert, Senator Dr. Joachim Son-Forget, Deputy Dr. Jean-Philippe Masson, President - FNMR Prof. Jean-François Meder, President - SFR

Olivier COLIN, mediator We will start our round table discussion with a representative from each of our parliamentary assemblies: Senator Alain Houpert from Côte d'Or and Deputy Joaquim Son-Forget, representing French citizens abroad in Liechtenstein and Switzerland. They are both radiologists.

I’d just like to say a few words before the debate starts. The President of the Republic has announced a 1.5 billion euro project that will be implemented up to 2022 and 400 million euros which will be devoted to breakthrough innovation projects. His objective is to make France a world research hub. Two major companies have already agreed to play the game, they are IBM and Samsung.

During this debate, we will talk about France's independence in relation to Artificial Intelligence. We will talk about financing as well as change in the legal framework. We might talk about education, and if we want this intelligence to win unanimous support, how to make citizens aware of the problem of artificial intelligence.

Jean-Philippe MASSON It would be interesting to hear Deputy Joaquim Son-Forget's answer to this problem for the French State, the investments required for a future tool that is almost strategic.

Joachim Son-Forget, Deputy

Joachim SON-FORGET Thanks for asking these pertinent questions. Maybe in the end I will agree with you. I will start with one thing, which is that everything depends on whether we have a purely geopolitical vision, like Florent Parmentier said earlier, or whether it is purely geoeconomic. The truth lies a bit between the two. In other words, you said that on one hand there is the dynamics of the States, because we're talking about French sovereignty

1 Re-transcription and summary under the responsibility of the editorial staff

III

Chapter III - Discussions - Round table - 177

over the control of these data, and maybe in extenso, radiology. But I believe that beyond that, the question simply applies to our field of activity as it applies to other fields of activity. The answer is mixed because of the investments which are certainly public and which are still to be made, even if it is so that our health care system can be reformed and modernized, but this also has to be done with private stakeholders.

In some ways this mission is what it's all about now. We shouldn't lack faith in this either. What we want now is to attract the attention of the people concerned with artificial intelligence here in France. Furthermore, there is an entire campaign in which I participate, notably with certain Asian countries, to attract people who are going to conduct research here in France, and who will conduct this applied research here in France. We are very good research scientists, as you know, which is why, as Professor Chang said, we can produce the father of artificial intelligence such as Yann LeCun, the father of deep learning, and that he can then escape us, at least temporarily, because he is back, but for a major American firm called Facebook. This is not an isolated case. The answer is mixed. Private investments are needed and we shouldn't be afraid that this is also applicable to our field.

I think that now we must fight, fight to know whether we must look after small private offices, our machine and the turf battle with the other

specializations. We will have to go beyond these dimensions.

There is something I find regrettable in the vision, and I am going to try to be your voice, because I believe we will agree on the subject. I'm sorry that teleradiology was not given enough importance. I think Senator Houpert will agree with me about that. Teleradiology was discredited as something that did not fit into the argument for the Social Security financing bill after all. Things are said to this effect, it doesn't have to be an argument by sub-specialization. It is in extenso telemedicine, tele-monitoring. But we must face the reality. Maybe we'll be able to, and we will certainly be accused of being corporatists, but we must ensure that this voice is heard.

Because the medical discipline that is most ‘uberizable’ is radiology. Maybe there are retinographs, electrocardiograms, let me tell you, people are thinking about it. This will start with the second opinion, it will go even further, maybe it will use blockchain technology to bypass intermediaries and facilitate data circulation. Tomorrow’s combat won't necessarily be the possession of data, but the possession of infrastructure that simply makes it possible to create a lucrative business, or even an extremely lucrative one in terms of medicine. This will start, and it could start with radiology. As radiologists, you're afraid of being eliminated


from the system, and in fact, I believe we all agree, it is not as simple as that. It's not as simple as someone like Laurent Alexandre puts it. I'm also teasing like Florent did a bit earlier. But I think that between the alarmist talks, the reality and a technically reliable analysis, there are two worlds. It's not urologists who will be giving radiologists lessons in artificial intelligence all the same.

Beyond that, this will not be the problem. You will or won't lose your job. The real issue is to know for whom you will be working. Another combat for the future is to know whether you are moving towards radiology that is in the hands of a globalized company which oversteps the sovereignty of States, or whether... I think this is the aspect that the combat for sovereignty will concern. But this is a vision that we don't have in the internal domestic debate concerning the Social Security financing bill, because we're focussing on local administrative management, and we're not thinking of that international competition dimension.

Alain HOUPERT We're both from Bourgogne.

Joachim SON-FORGET There are three Burgundian radiologists who are members of Parliament.

Alain HOUPERT In Bourgogne, there used to be a great minister

Alain Houpert, Senator

of Louis XIV whose name was Vauban and he used to say there is no unassailable fortress, there are only ladders that are too short. Philips invested one billion in one year. We are going to invest one billion in four years. I don't think we're using the right ladder. I'm afraid that radiology will become the new steel industry that we kind of abandoned. I'm a bit older than our colleague. I knew the silver image, the old French societies and France was really in advance in radiology. We have highly regressed. Medicine is going to become the new steel industry. You know, the Lorraine steel industry was completely abandoned. I'm afraid of that.

Our colleague talked about telemedicine. In 2009, I was in the Senate, and I fought for it to be written in the stone of the law, because our colleagues and the people in the ministry wanted telemedicine to exist, but for the radiologist to be beside the patient. What is the benefit? We had to fight. We had to convince our fellow


senators. It was in the Senate that we started to do things, and then the Assembly followed. Because the Senate is a bit like an assembly of wise people, but it is important to have members of parliament like you, like me, who have our feet firmly planted on the ground, and who address the Senate or the Assembly with winged feet like Rimbaud, to speed things up. The danger, exactly as you said, is if this uberization of telemedicine takes place in other countries, because with the principles that we have in France, if we leave things as they are, miss opportunities, don't take the right measures, we'll have telemedicine centers in Dubai doing low cost radiology interpretations in medical deserts. And as a result, there will be a legal risk. We will have unsigned reports and the patient will have no recourse. Yes, to artificial intelligence in the patient's interest, but no, to unrestrained uberization.

Applauses

Jean-Philippe MASSON There, it's said and it's quite true. It's true that G4 1 led to the moralization of teleradiology through the charter we drew up a few years ago, and which moreover, was co-signed by the CNOM.

Actually, I think there is a gap. There is now an open space that needs to be filled.

In fact, radiologists are avid artificial intelligence enthusiasts. I'm not worried about the development of artificial intelligence for radiologists because it is in our genes. We have always been able to change with the times. We have always been able to change with the new technologies that appeared, whether it was ultrasound, even though we weren't that good at it because there were many who didn't believe in it and who let it go to other specialists, or CT, MRI and all the new sequences that appear regularly, all the tools.

1 The National Board of Radiology (G4) which groups together CERF, FNMR, SFR and SRH.


We experienced the beginning of Artificial Intelligence with CAD 2. We use them every day and we have no problems. We are happy when we are able to have a new more efficient CT scanner, with more tools. Artificial intelligence, as Paul Chang said earlier, will be implemented both in our machines and our spirits without difficulty. The difficulty that I see, and for this I turn to my politician friends, because they are the ones who make the laws, and those who make the laws allot budgets, notably for radiology. We know all the same, that for some years now there has been a trend towards lowering rates in radiology, and notably in slice imaging. These artificial intelligence tools will inevitably be expensive. Considering the change or even the maintenance of rates, I'm not at all sure that this change will be sufficient to enable French radiologists to invest in these tools.

Because in reality, the investments we make in these tools, it is especially for our patients that they are useful. It's because of these same tools that we're able to make better diagnoses. Do you think we could achieve better stabilization than we did by signing the summary of conclusions based on pertinence? All the same, it's a summary of conclusions that will generate a certain number of savings off the back of radiologists again. 2 Computer-aided Diagnosis

We arrive at the end of this situation because the problem is that radiologists can't invest any more. The manufacturers themselves say that France is becoming an emerging country because we no longer have the means to invest in good equipment. Do you the politicians think this will continue? Or do you think that finally, it will be understood that there is a need to give radiologists an advantage, especially with the advent of these new tools?

A speaker In terms of heavy equipment, we are practically on the same level as Turkey. Yesterday, Agence France Presse had a title saying that artificial intelligence detects more skin cancers than dermatologists. That means they have an algorithm that detects 95% of cancers, in comparison to 86% by dermatologists. Should radiologists be afraid of artificial intelligence? I don't think so because radiologists have always been in advance. We are the only ones to really have structured offices, with secretaries and operators. It is true that our profession changes tremendously. All our offices have always been research micro-laboratories, and if our manufacturers are progressing, it is because we were progressing in semiology. It was radiologists, for example, who made the advancement in the diagnosis of Down's Syndrome through nuchal thickness. The manufacturers developed the machines to enable progress.


I am not pessimistic. I think radiology is faced with the automation of procedures, through the decrease in prices, and artificial intelligence will be a good thing for the patient for screening many things, cancers. For example, for multiple sclerosis or asbestos diagnosis, much more precise screening can be done. Because we are doctors, supporters of humanity, we can really take care of patients.

Earlier, we spoke about listening, the patient wants to be heard, and it was 15 seconds, I think. It is important that radiologists go back to being consultants, orchestrators. Radiologists are different from other doctors, because they're in a three-way relationship between the referrer who requests the imaging examination, the patient and himself. It's an infernal triangle. In communications it is called the Karpman triangle: persecutor, savior and victim. Actually, they could devote themselves to that. They wouldn't be persecutors, but they would be saviors, they would devote themselves to their real profession; make diagnoses, and support patients on the path between the prescriber and the referrer.

Joachim SON-FORGET I'm in complete agreement with my colleague. With regards to dermatology, it's interesting, but if it concerns your own blood workup, you would prefer to trust a machine than a laboratory operator who counts cells. It isn't impossible that the same thing happens with our specialization in the future. Now, we must ask ourselves the question

of what we have left in terms of added value? It requires reconsideration. The odds are also that..., it was Florent who said it, he was presenting his long-term planning scenario on self-reading ultrasounds to select babies, but it is already here.

I'm going to help Mr. Philips, who was here earlier, because his images were not as good as those of the previous colleague, so I'm going to give him a hand. They already have a product. You connect it to your smartphone, it's Unified by Philips, it is still expensive. It's not impossible that in the future there will be small ultrasound machines available for any ordinary person. They will come to you and say: "there is a lump there, it's greyish. I know what that means." That means that the semiological reasoning must change, whether or not you want it to.

Instead of being afraid of it, I think it is rather an advantage because we see it already. We are shouted at because we do too many examinations, but in fact, it is because we are asked to do examinations. This trend will increase with the democratization of tools, small radiology tools. This will continue to replace radiology to a great extent right in the midst of required first line expertise, a place that standard semiology had before, that you were taught before. You all think that a clinical examination should be done, an interview, but nevertheless radiology will be more and more on the front line everywhere. That is what is left for you, you must believe it.


Dr. J.-P. Masson, Senator A. Houpert, Deputy J. Son-Forget, Prof. J.-F. Meder

It isn't taught, we kind of learn it on-the-job. We don't teach this somewhat refined, somewhat practical knowledge to our residents and it's a pity. You do it for those who do chests, you "mip" so as not to miss your lesions, but that is empirical knowledge. We could codify things to develop this technical competence

There is a second thing that remains when the current is turned off, or when you don't have all these algorithms, it's your capacity to perceive which you develop through your expertise. It's experience. Unfortunately, it isn't exploited enough in training, because in that respect I think there is tremendous diversity in practices.

I'm from the cognitive sciences, I did a double Major. That is why I went to Switzerland, I'll explain everything. I did a first Masters at the ENS, after that I went to Switzerland to the École Polytechnique in Lausanne. I did a PhD in neurosciences there. What we work on is all the cognitive biases and the conscience, and a bit on timing mental activities. It is true that in radiology, we could be even more efficient in the way we make diagnoses by having good knowledge of our own attentional limits, good knowledge of the way we cast our regard on images.

more finely. I won't bother you with it, but I do it for example, for my multiple sclerosis cases, and for my brain metastasis cases. I do the same thing, but most people don't do it.

These are little things. There is virtual reality, enhanced reality, everything you could ask for, but there is still the physical reality of your own knowledge and it isn't taught. It's a real added value that we still have, that the other clinicians who read images without knowing this type of thing won't have. We must also think of human resources and what more we can develop in terms of added value in our field, and in terms of improving efficiency so that, as Professor Chang said, instead of spending our time rebuilding things, doing low-level data processing, we invest in order to save hand time.

This is basic ergonomics. It starts with your poorly configured


ANNOUNCEMENT OF THE LAUNCH OF AN ARTIFICIAL INTELLIGENCE ECOSYSTEM

Jean-Philippe MASSON At the end of the conference devoted to artificial intelligence that the FNMR just organized in Lyon with Jean-François Méder, we wanted to announce that G4 - which is the French Professional Radiology Council, comprised of the French Board of Radiology Teachers (CERF), the French Radiologists Union (FNMR) and the Hospital Radiologists Union (SRH) - will address a tremendous void that became visible as a result of all the talks today. G4, in other words, the French radiologists, is going to launch the creation of an artificial intelligence ecosystem which will be dedicated to medical imaging.

In France, thanks to the activities of private practice and hospital radiologists we have big data of over 500 million records, that contain image files, of course, but these image files are all correlated with the reports. These files are archived by the doctors in France, in their departments, under good conditions, as we know.

This new technology will provide real medical progress which will serve our patients. We're convinced. We know it's a real medical and technical challenge. This challenge, this project we intend to carry out with

Jean-François and all the other representatives of French radiology, will be carried out and as we heard today, it is essential, to do so in compliance with the ethical and legislative rules of our country.

We hope that through this project the independence of French radiology will be maintained, and that it will respect the humanistic vision that we have always used to serve our patients.

Jean-François MEDER Thanks very much Jean-Philippe. I would simply like to emphasize the fact that sometimes we have been, if not reproached, at least reminded that this is a step by French radiology in general, beyond the world of practice, beyond the barriers that might have been built by others. It's a wonderful step that is supported by all four constituent members of G4. I would really like to emphasize this fact. Thank you.

Applauses


computers. Your mouse is not adapted because it's not the same as mine. I have ten shortcut buttons that put all my operations at my fingertips. And all these things need work. This is about educating our young residents so they can acquire special competences that the other clinicians cannot have unless they receive this education. We need to think of that as well.

Jean-François MEDER Two points. The first is that artificial intelligence, as we said before, will make it possible to increase productivity. Nonetheless, I would like to remind you that France is the world champion in the use of machines. Our machines are saturated, as you know very well, and we have absolutely no time to make any... I don't see how we can increase. It's impossible. That's the first point. The second point is that in the Villani report, which I didn't learn by heart, but which I browsed through, the words medical imaging appear six times. If it is really a priority, it should appear more often. If it is really a priority, we should be invited by the auditors. Instead, we went and were audited because we asked to be audited. We must admit that this is not normal. We're hardly ever cited. Your comparison with the steel industry bothers me terribly, I like it. Because I would also like to point out that at present, the purchase of low cost equipment in France is becoming completely commonplace. We

used to be the radiology flagship of the world. Today we are pauperizing our specialization, and that worries me terribly. Applauses

Olivier COLIN I would like to ask the politicians to give their immediate reaction to this statement.

Joachim SON-FORGET I've been going to radiology conferences for years, where I hear talk of big data and artificial intelligence from people who sometimes don't understand what they're saying, but who repeat the same presentations each time, without any discernment.

At some point, we must take the bull by the horns, the initiative you have must be supported. Because, if we want to make France and Europe a stakeholder in artificial intelligence, which they aren't yet, but for which they have the potential, someone has to lead the way. What would be better than doing it in the medical professions, because they need to be modernized? You need to lead the way. Who can do it better than radiologists, because you know how to adapt? In fact, you've gone from the silver image to CT and MRI, and all the known modalities. But today, there are still technological leaps to take. It is up to the radiologists to do it.


You could be a good flagship for the application of the big data principle. We could be a good flagship for the application of the big data principle in France, and maybe a European model. I would like to thank you for this initiative. Know that, as a legislator, I will do my utmost to support you. I have included my colleague Anissa Kheder, she will take a stand as she sees fit, but at least I would have presented her with the facts. She is a Lyon Deputy and I advised her to come and listen to you today. We will try to advance these ideas in the French Parliament. Thank you.

Applauses

Alain HOUPERT Like my colleague, I say bravo. Imagine if the GDPR 3 were in practice during the Renaissance, I think André Vésale and Ambroise Paré wouldn't have invented anatomy and we would still be in the Middle Ages. We mustn't be afraid to look ahead. France is the country of lights. We, radiologists are searching for the light in the darkness.

A few years ago I went to the HIMS in Tampa, Florida with my colleague Jean-Philippe. It's the largest American medical data processing conference. I was impressed because we were received by an American senator who owns a hospital and who invests millions of dollars each year. He sells completely anonymized data for one million dollars, and that enables him to develop 3 General Data Protection Rules

his market. We must move forward, in France we need to stop missing the right train because we're always late.

Remember Hounsfield. When he invented the CT scanner, he went to see a Batavian manufacturer. He had to present his dossier, he didn't believe in it. He returned to England and saw his friend John Lennon who told him, "come over to my place, my boss is the owner of the record company EMI and he has a lot of money. I think he will help you." The owner of EMI was entirely won over by Hounsfield's discovery and the first CT scanners were by EMI. I worked on it. Then EMI sold their license to a Batavian manufacturer. We wasted time. Let's not waste time, let's go, it's important. Earlier, we talked about Estonia, we talked about China. We talked about Israel. Those places where there is research on artificial intelligence are the countries where there is spending on defense and security. I go to Israel often. It's very important because it's a country where start-ups develop at top speed. It's a country where they're capable of soul searching. Let's be like Israel. The last time I went to Israel I met Shimon Peres. He said to me, "you're too pessimistic in France. There are two ways of being optimistic: either you see the glass as half full, or you continue to fill it up." I, a Senator from Mont Rachais, I continue to fill it up.

Applauses


Jean-Philippe MASSON To answer you, I find that we, radiologists, show quite exceptional optimism and ambition. The glass, we really see it as half full and even very full.

A speaker How will you organize the availability of image dataset for private offices? What are their constraints? On which platforms? How can you organize this common interest?

Jean-Philippe MASSON First of all, we're going to start gradually. Private offices have been equipped with PACS for a number of years now. I don't remember the date when the fixed rate for archiving came about, before it was cancelled, but it has been about ten years now that the government has understood the importance of assisting private practice radiologists. First there was the fixed rate for digitization, and then there was the fixed rate for archiving. And everyone took advantage of them, as long as they owned a PACS. At present, practically 100% of the private offices are equipped. That is why, when I read in a news item that following the hospital exhibition week, there was a shout of joy from the FHF saying, 'it’s fabulous, 70% of the hospitals finally have a computerized record for their patients." It makes me furious because there were three or four digital, financial, data processing plans to help hospitals and they are still only at 70%.

On the other hand, private practice radiologists received no aid for information systems, which is the equivalent, and we are still equipped.

We are equipped with PACS that archive in DICOM. There won't be so many difficulties to transmit information. The only difficulty we could have is with harmonizing because there is no national unique identifier. It will be finalized because following instructions by ASIP the ministry has discovered that there is a national identifier that can be used, which is the social security number. This will be something important and as Paul Chang pointed out, this is an advantage we have over the United States. Hospitals, most of them, the major image producers are all equipped with PACS. The entire infrastructure now remains to be established and that will be complicated. Each of us, in our divisions, will designate a manager. I imagine there will be a sort of board of directors that will oversee the organization of all this.

I will answer the next question immediately even before you ask it. What about financing? I don't know, but I think we must choose from among all the companies that will be ready to pounce on our hundreds of millions of files. "

A speaker Before talking about image in artificial intelligence, there is that problem of data protection


that is really a risk that could drag us down. Think of the Mediator scandal. I'm talking to the gastroenterologists. Do you remember the Mediator scandal? One of our pneumologist colleagues had to fight, and was shouted down by the media before people finally listened to her. If we had all the clinical data of anonymized patients, we would only have had to push the Mediator button and we would have seen all that could cause.

Let's move forward, we are radiologists. We are doctors, practitioners, bearers of humanity, but most of all, entrepreneurs, people looking to the future. I think we can make a contribution.

A speaker I have a question, because at the beginning of May I was at the Collège de France where a member of the AP-HP gave us the details of the Paris hospitals project for their database. One of the questions asked was, what do we do with the data? They have a particular concern bordering on paranoid, of not allowing data to be stolen by people who then sell algorithms back to them in software they will use.

Jean-Philippe MASSON We're quite in agreement. Actually, this will be one of our problems. There is a reason why we had a lawyer here today, who explained 4 Francophone Radiology Days.

who owns radiological data. We prepared this conference so that we could make the announcement we're about to make. We will actually have to measure the weight of the data we are going to provide, versus the algorithms that we will be offered. It will be a deal.

Once again, I don't know the details. We will set this up calmly. Maybe we will meet again in one year to take stock of things or maybe we will talk about it even before at the JFR 4. In three or four months, in October, we will have made a bit of progress on the subject already and a certain number of things can be clarified.

Patrick GASSER, President - UMESPE First of all, I'd like to say bravo for this initiative. I think the entire profession should be included. You've said it all. We talked about France, Europe, and it was mentioned that we need a unifying European project. Therefore, I think that at some point we must open up quite widely to all specializations. Moreover, I understood all the same, that to provide and create AI, diagnosis is required. And diagnosis, as you know, we often have it, not at the same time as the imaging, but sometimes after. It is important to enter the right data. I think everyone has a role to play in this. It is a factor that could be unifying in the future. As president of the UMESPE, you understand, I must be a unifier in this field. With regards to financing, at


present we finance registers in the framework of ONDAM 5. This type of financing must be provided, both in the agreement and in the PLFSS 6 for next year, for example. But our politicians should do this in the future.

Joachim SON-FORGET I agree with you wholeheartedly. I won't have the corporate reflex, because everyone must be included. Do you remember the story of PACS once we passed it and had beautiful tools, reconstruction tools, 3D, etc.? We gave the attending physician a type of poor-quality sub-viewer where you see the images all pixelated, they can be passed one by one, every four seconds. This was a way of saying that he didn't have the same tools as us and that he wasn't as good as us. It's childish, and we got over it. We're only in the process of getting over it.

Along the same lines, everyone has to be included. And why? Because the radiology profession would have been the flagship. But then, if we include all the other specializations, all at once… I'm giving you a consultation here, to convince an administration like the French administration. Because the political will might be there, but there might be good and not so good ministers. I think we have a good minister. But on the other hand, to convince all those who come with the weight of administration, and I'm not targeting anyone in particular, but we are 5 Objectif National des Dépenses d’Assurance Maladie (French Social Security Expenses Objective) 6 French Social Security Financing Bill,.

faced with it every day, like members of parliament. It's not necessarily a problem of convincing the minister and her cabinet. It's all the people who slow you down at different levels.

So, if you include the entire medical profession with you, it's a lot easier and no-one can argue against you that, "yes, you radiologists, you want to make some more money. So, you're trying to overtake everyone." In this way, at least we tackle it head-on. Even if we are at the outpost, I think we must include the others, and in fact, we would improve the data quality.

A speaker Thank you very much. I agree with you, at least partially. I was talking with our colleague earlier. I think in France, we have a serious problem with training. We have a common initial medical training, and then afterwards, silo specialization training, which is therefore corporatist. We drag this corporatism around with us for all our professional lives. What Jean-Philippe suggests, because we must acknowledge that Jean-Philippe is the initiator of this great project, I think that even though it might not be immediate, it's a beautiful way of uniting the specializations that want a rapprochement. We're in France, a parochial country.

Moreover, we showed it at the ministry when we had to consider the authorization for interventional radiology. We asked for it to be interventional


imaging, because radiology, like other interventional activities, will probably share common tools with others. Radiology is a great training pathway for image-guided treatment, as are others. We're open, but there is that corporatist spirit which we must acknowledge, therefore, it might take a bit of time.

Jean-Philippe MASSON I would just like to specify that this project is not for earning money. That is not the aim at all. I don't know which legal framework will be chosen. The idea is really to make a tool available to the radiology profession so that radiologists can work on it.

That is why I told you earlier, 400 million files is a bit like an exchange currency with manufacturers. We offer them French radiology data and in exchange, they will offer radiology products and tools which will have contributed to this database. Afterwards, what will happen in the rest of the world? Because these tools shouldn't be limited to France, because radiology is radiology. Maybe at that point a sales discussion should be set up.

But with regards to our French territory, the idea is not to make money. The idea is really to create a tool that takes into account our special features, the way in which we work.

Joachim SON-FORGET We spoke about Estonia, and I would also like you to go and observe this type of country which is maybe smaller than France but which have succeeded in making considerable digital transitions. Taavi Kotka, the former head of digitization in Estonia will attend a conference I'm organizing in Switzerland in October. They used blockchain technology to verify identities, to create an identity register. This is one example among many of this country' accomplishments. But you should really go and see, make the benchmark, go and see the good examples that have been made, even in other fields, but of course imaging, and draw inspiration from it.

We really have a chance, I think, to be a symbol of success in artificial intelligence, in practice, and finally in the modernization of medicine. Remember that these are two objectives that were announced in Emmanuel Macron's program. I campaigned with him from the very beginning, even before he was here campaigning. I know that these were things meant a lot to him, artificial intelligence, modernization of the health care system, and digitization of the entire administration in general.

We are participating in the effort on both sides, and that is really what should be emphasized so there can be no more arguments against the profession. "Yes, but teleradiology can't be a subdivision of telemedicine, etc."


Finally, the argument is no longer admissible once you are the driving force behind reform.

Alain HOUPERT Earlier, Jean-Philippe asked, "can we still invest?" Every year we vote for the PLFSS (French social security financing bill) and every year, radiology, regardless of whether it is private or public, has fixed costs. In other words, each time we reduce the full-time equivalent in hospitals and for the liberal professions, the contributions are decreased. I think it's a negotiation tool that would be very important for the profession to have some peace, and stop being stressed, whether we're in a hospital or private practice. Not stressed for ourselves, but because we are business managers, while you are department managers, you are executors, and you reduce in order to economize. What I regret is that the social security financing bills are always earnings, contributions and expenses. In fact, we should see all the earnings in a global holistic manner. And the real earnings is all the money that is created by employment by the pharmaceutical industry. When generic drugs were created to economize, we killed the French pharmaceutical industry. All generic drugs are made in India and China. 20% come back adulterated, because they come in containers. The quality and monitoring are always by short channels. This is great news, because it will enable us members of parliament, there are three of us, 7 French High Health Authority

but there can be more, to ensure progress and negotiate an intelligent PLFSS.

Robert LAVAYSSIERE Jean-Philippe, you asked the members of parliament a question. Senator Houpert just, quite partially, answered the question about financing and the regular decrease of our means in proportions that are becoming irrational. We also know that in some cases the value of our procedures fall even below a general practitioner's consultation fees. In this case we are embarking on a project which is quite extraordinary and welcome, in a context that appears a bit wild. Our friend Jean-François Meder really emphasized the fact that we were in the process of pauperizing our equipment, and that it was a very bad tendency. I mean, even in the university hospitals we see CT scanners that are no longer at the level they should be in terms of research.

My question was a bit different, since that remark was made. Earlier, I had an interesting discussion with Luc Soler about his product, if you can call it a product. As you know, we have an organization in France called the HAS 7, and for some years now we've been constantly reminded of the rapid innovation management procedure, and for those of us who have tackled this sector, we realized quite soon that we ran into obstacles that were, in fact,


set up so that there is no progress. Earlier, Mr Soler explained that this child, let's take the example of nephroblastoma, had been able to bypass dialysis and transplantation and after the use of procedures guided by the system they had developed, was in perfect health. So, of course, savings were made on the health status of this child who didn't have to suffer through all that. That is extraordinary as it is. In general, society has also economized on related expenses. Moreover, Luc Soler has submitted files. He was constantly asked for an escalation in the number of patients, and finally, they got to the point where they decided to get out of potential financing of the innovation sector with which they've been wearing us down for years. The question I would like to ask is, will you or won't you be able to make this system advance, instead of becoming engaged in insignificant and purely honorary activities?

Alain HOUPERT This morning before coming I was engaged in a few insignificant and purely honorary activities. That is part of our work we would prefer to be… It is true that it's not easy to be a member of parliament. Because during the week we're in Paris, and on the weekends we are… in congress, engaging in trivialities. But you raised a major issue that my colleague raised earlier: administration. There is the ENA, and that is probably why we deleted the circumflex accent,

because when they used to wear a dunce's cap and they looked at themselves in the mirror, they didn't recognize themselves, because they saw ‘ANE’ with a circumflex accent (ÂNE =donkey). But there is also the school in Rennes, which was inflationary in all the hospitals. We had hospitals that were working well, we created flagships, multi-storied cemeteries, places where patients don't feel acknowledged. There used to be a French system that was multi-winged that used to work well, that was human. I remember when I was a resident, we felt good at the hospital. I see the young people today and they don't even know each other, they've become technicians. We need to reverse the trend. President Macron said: "reverse the trend". The administration is not there to guide the country. It's the politicians who should guide the country. The administration should serve the politician, and as long as that isn't the case, the trend will not be reversed.

Applauses

Joachim SON-FORGET There are also good ENA students. There is one at Matignon and the Elysée. But, in fact, there is still a training effect at other levels of the administration. It's not necessarily the fault of the schools that do the training, or the fault of separate individuals. The system in general sometimes creates a somewhat entropic effect which is even sometimes voluntary. We acknowledge it as politicians. We can share this acknowledgement because it is always


a sort of balance that is unfortunately too stable, between a politician who wants reform, regardless of the side he's on, but who wants to pass reforms, and an administration aimed at stabilizing the system, especially during transition periods. It is useful during political transitions, but it can end up being the first obstacle when you try to change things. I have nothing against any individual. That is why, if you want to make a project such as yours succeed, you have to make it succeed at all levels. The same way as when we want an idea, a law or an amendment to be accepted, we also do our part by running our press campaign. We run lobbying campaigns by group of deputies and senators who rally together. Sometimes we look for support among other parties. "No, you shouldn't do that." Yes, I do it anyway. It's my Swiss side, I really like working with people from other parties. You will be able to do it as well. To make the project a success, you must come with a big project. I hope that at the next stages, like the JFR, the intention will have already been transformed into something concrete. Because you need to be careful with the political deadlines. In fact, for us, it is the Social Security financing bill, and that will be soon. If it isn't for the first time, it must at least be for the next time. But that means that in the same way you all submit your abstracts for the next conference, you must anticipate the results that you haven't acquired yet,

six months before. We all know how it is done. I shouldn't have said it. You must do the same thing for this subject, anticipate, and come and flood us with these arguments that we can both understand. I imagine that our colleague Didier Martin, who is the Deputy from the Côte d'Or, could hear it as well, because he is also a radiologist, but you will have to convince the other doctors in the Assembly and the Senate, and most of all, the other non-doctors in the Assembly and the Senate. That will be a tough job, because you'll have to get past the subject of corporatist lobbying. We are waiting for you and remain open to these proposals.

Jean-Philippe MASSON To close this day, I would like to thank all the participants, first the speakers, our politician friends, our colleagues and politician colleagues who were kind enough to attend. Of course, I would like to thank Paul Chang who came a few thousand miles from Chicago to be here, and obviously we will be going to see him in December for the RSNA, that is for sure.

I would like to thank all our industrial sponsors for their assistance. I know some of them were very interested in the announcement that was going to be made. We are now waiting for their proposals.

All the talks that were made were video recorded and will be available progressively on the FNMR

Chapter III - The Discussions - Round table - 193

website. All the talks were recorded and will be made into a book that will be presented at the Francophone Radiology Days (JFR) in October. The text in English will also be available in PDF format so that Paul can transmit the good French message to our great American cousins.

Applauses

IV - A French AI ecosystem


IV- CONCLUSION

A French artificial intelligence ecosystem dedicated to medical imaging

To wrap up the conference, Dr. Jean-Philippe Masson, President of the FNMR, and Prof. Jean-François Meder, President of the SFR, announced the upcoming launch of a French Artificial Intelligence ecosystem dedicated to medical imaging. Following this announcement, the participants in the conferences responded and asked the two presidents questions (see the round table discussion section). Senator Alain Houpert and Deputy Joachim Son-Forget, both radiologists, gave their first reactions and showed their support for this initiative.

See over for the press release.

Dr Jean-Philippe Masson and Prof. Jean-François Meder

Press release

Paris, June 4, 2018

Artificial intelligence: 500 million medical imaging records to serve patients. Radiologists prepare the future.

Doctor Jean-Philippe Masson, President of the FNMR (French Radiologists Union), and Professor Jean-François Meder, President of the SFR (French Radiology Association), announced, on behalf of G4 1 (French Professional Radiology Council), the launch of a project for a "French AI ecosystem dedicated to medical imaging". This system will not be dependent on giants such as the American GAFA and Chinese BATX. This announcement was made at the end of the conference entitled "Artificial intelligence, dream or nightmare for radiologists?" organized by the French Radiologists Union. It was held on Saturday June 2, 2018 in Lyon in the presence of international experts representing the scientific, political and philosophical communities, as well as representatives of civil society. Thanks to potential big data of over 500 million medical imaging records

and particulars and the exhaustive nature of the radiology reports archived by the radiologists in the French imaging departments for 5 years, this tool will use a new technology, bearer of medical progress to serve patients. This project should meet the medical and technical challenge that the integration of this big data represents. It should also comply with the ethical and legislative rules. It will ensure the independence of French radiology by respecting the culture of Francophone medical imaging as well as its humanistic vision so that this breakthrough technology always makes the patient the focus of the health care system. The initiators of this project, supported by G4 (the French Professional Radiology Council), which is a part of the French digital development project, have confidence in the full support of the French State.

1 G4 : The French Board of Radiology Teachers (CERF), the French Radiologists Union (FNMR), the French Radiology

Association (SFR) and the Hospital Radiologists Union (SRH)..

Chapter IV - A French Artificial Intelligence ecosystem - Press Release - 197


- SPEAKERS

Prof. Jean-Michel BESNIER emeritus professor of philosophy Sorbonne-University [email protected]

Mr François BLANCHARDON President - regional board AURA FRANCE ASSOS SANTE [email protected]

Prof. Paul CHANG Radiologist University of Chicago [email protected]

Dr. Alain FRANCOIS President - Regional Union AURA Radiologists [email protected]

Mr David GRUSON Health Chairman Sciences Po Paris Ethik-IA [email protected]

Mr Aïssa KHELIFA Sales & Marketing Director AGFA HEALCARE IT [email protected]

Mr Cedi KOUMAKO President - UNIR [email protected]

Mr Christophe LALA Managing Director G.E. [email protected]

Dr. Robert LAVAYSSIERE Vice-President - FNMR, Radiologist [email protected]

Mr Jean-Baptiste LEPRINCE Services Director SIEMENS HEALTHINEERS [email protected]

Dr. Jacques LUCAS Vice-President - CNOM [email protected]

Mr Antonin MARCAULT PHILIPS Development Manager Innovation & New Business [email protected]

Dr. Jean-Philippe MASSON President - FNMR, Radiologist [email protected]

Prof. Jean François MEDER President - SFR, Radiologist [email protected]

Mr Florent PARMENTIER European geopolitics expert Sciences Po Paris [email protected]

Prof. Luc SOLER President - Visible Patient IRCAD [email protected]

V

mailto:[email protected]



















Mrs Laure SOULIER Lawyer Cabinet Auber [email protected]

Mr François VORMS Managing Director CANON [email protected]

Section V - Speakers - 199




Publications Director: Dr. Jean-Philippe MASSON

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October 2018

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Documents

All copying, partial or complete, by any process ... · use of CRISPR-Cas9, the famous molecular scissors which will make it possible to copy and paste genomes. As a philosopher in