5 Reasons Why Radiology Needs Artificial Intelligence

5 Reasons Why Radiology Needs Artificial Intelligence

Created on October 25th, 2016Simon Harris, Managing Director & Principal [email protected]

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© 2016 Signify Research 2

Global shortage of radiologists1

5 Reasons why radiology needs artificial intelligence

Enhanced productivity2

Better diagnostic accuracy3

Lower rates of misdiagnosis4

Improved patient outcomes5

BackgroundThe information in this presentation is taken from a market analysis report from Signify Research titled “Diagnostic Analytics – World Market – 2016 Edition”. See slide 10 for details.

Reason 1 - Shortage of radiologists in many countries



US105

DE98

UK49

ES112

NG2

BR21

ZA17

SA35

AU86

JP46

In most countries there is an insufficient number of radiologists to meet the ever-increasing demand for imaging and diagnostic services

The situation will get worse, as imaging volumes are increasing at a faster rate than new radiologists are entering the field.

Cognitive computing techniques, such as neural networks, deep learning and predictive analytics, may help by improving the productivity of radiologists

≥100 radiologists per million population

50 - 99 radiologists per million population

<50 radiologists per million populationSources: National radiology societies and government agencies. See slide 9 for details


© 2016 Signify Research

Reason 2 - Enhanced radiologist productivity

1 Smart alerts to regions of interest

2 Automatic image annotation and quantification

3 Faster access to patient information held in EHRs and other systems

Compare readings with images, diagnoses and outcomes of similar cases4

Create draft reports for radiologists5

4

Reason 3 - Better diagnostic accuracy



Reduces human error

Accurately tracks the growth of lesions and tumours over time

More quantitative and more objective diagnosis

An automated second opinion

Expedites early interventions



Reason 4 - Lower rates of misdiagnosis

the average error rate of radiologists for unselected cases1

the average retrospective error rate of radiologists1

of radiologists in the US will face a lawsuit2

of the women who get annual mammograms over a 10-year period will have a false-positive finding3

4%

7%

50%

The average radiologist reads approximately 15,000 cases per year.4 Assuming a 4% error rate, on average radiologists will misinterpret about 600 cases per year.

Machine learning algorithms can alert radiologists to disease indicators.

Cognitive computing can mine patient records and medical literature to provide radiologists with relevant information, and to compare new cases with existing ones.

Radiologists suffer from fatigue, computers do not.

30%


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Reason 5 - Improved patient outcomes

Problem Benefits of Cognitive Solutions

Late detection and diagnosis of disease makes treatment less likely to succeed and reduces the chances of recovery

Reduced reading times. Longer term, algorithms will be embedded in imaging scanners for immediate detection of disease at the time of the scan.

Urgent cases are not always prioritisedReview scans in real-time and automatically escalate prioritycases within the radiologist’s reading queue.

Incidental findings are often not followed-up

Automatically read radiology reports to look for incidental findings, extract relevant information, schedule follow-up exams and prompt the referring physician to action the follow-ups recommended by the radiologist.

7


Barriers to mainstream adoption of cognitive computing in radiology

More clinical evidence is needed regarding the performance of machine learning algorithms in radiology applications

Resistance from some radiologists who see cognitive computing as a threat

High level of scepticism regarding existing (not machine learning based) computer aided detection solutions, e.g. CADe for mammography, due to “alert fatigue”

The “black box” nature of machine learning algorithms can undermine radiologists’ confidence in the results

The regulatory bodies, e.g. FDA and CE, have taken a cautious approach to approving solutions that use machine learning techniques

Most of the current solutions are for specific use-cases, e.g. detection of lung nodules in chest CT scans, but radiologists typically require a comprehensive “tool kit” with a suite of algorithms capable of detecting a wide range of conditions across multiple modalities

Machine learning algorithms for detection will gain acceptance in the coming years; however, it will likely be at least 5 years, and possibly many more, before computer-aided diagnosis becomes mainstream

Machine learning algorithms for detection will gain acceptance in the coming years; however, it will likely be at least 5 years, and possibly many more, before computer-aided diagnosis becomes mainstream

More clinical evidence is needed regarding the performance of machine learning algorithms in radiology applications

Resistance from some radiologists who see cognitive computing as a threat

High level of scepticism regarding existing (not machine learning based) computer aided detection solutions, e.g. CADe for mammography, due to “alert fatigue”

The “black box” nature of machine learning algorithms can undermine radiologists’ confidence in the results

The regulatory bodies, e.g. FDA and CE, have taken a cautious approach to approving solutions that use machine learning techniques

Most of the current solutions are for specific use-cases, e.g. detection of lung nodules in chest CT scans, but radiologists typically require a comprehensive “tool kit” with a suite of algorithms capable of detecting a wide range of conditions across multiple modalities

Potential for legal complications. What happens if the cognitive solutions gets it wrong?

8© 2016 Signify Research

References



Estimates for the number of practicing radiologists by country were obtained from a variety of national radiology societies and government sources, including Royal College of Radiologists (UK), Société Française de Radiologie (France), Deutsche Röntgengesellschaft (Germany), Sociedad Espanola de Radiologia Medica (Spain), Radiological Society of South Africa, Association of Radiologists in Nigeria, Radiological Society of Saudi Arabia, The Royal Australian and New Zealand College of Radiologists, Japan Radiological Society, American College of Radiology, Colégio Brasileiro de Radiologia e Diagnóstico por Imagem (Brazil).

Other references:

1. Radiology Quality Institute, Diagnostic Accuracy in Radiology: Defining a Literature-based Benchmark2. Jena AB, Seabury S, Lakdawalla D, Chandra A. Malpractice risk according to physician specialty. N Engl J Med 2011;365(7):629–636. CrossRef, Medline3. American Cancer Society4. Bhargavan M, Kaye AH, Forman HP, Sunshine JH. Workload of radiologists in United States in 2006-2007 and trends since 1991-1992. Radiology. 2009;252:458-467.


About Signify Research

At Signify Research we are passionately curious about Healthcare Technology and we strive to deliver the most robust market data and insights, to help our customers make the right strategic decisions. We blend primary data collected from in-depth interviews with technology vendors and healthcare professionals, to provide a balanced and complete view of the market trends.

Whether our research is delivered as an off-the-shelf report or as a consultancy project, our customers benefit from direct access to our Analyst team for an expert opinion when they need it. We encourage our clients to think of us as an extension to their in-house market intelligence team.

Our major coverage areas are Healthcare IT, Medical Imaging and Digital Health. In each of our coverage areas, we offer a full suite of products including Market Reports, Customer Insights and Vendor Selection Tools, as well as custom research and consultancy services. Our clients include technology vendors, healthcare providers and payers, management consultants and investors.

Find us on the web at www.signifyresearch.net.

Simon HarrisManaging Director & Principal Analyst

Simon has 23 years of experience in technology market intelligence, having served as Executive Vice President at IMS Research, a leading source of research and analysis for the global technology industry. Whilst at IMS Research, Simon established the InMedica brand of medical market research. IMS Research was acquired by IHS Inc. in 2012 and Simon stayed on for four years as Senior Research Director for the company’s Technology market intelligence division. Simon left IHS in March 2016 to launch Signify Research.

[email protected]+44 1234 436 150

Diagnostic Analytics - World Market Report – 2016 EditionSignify Research recently published a detailed analysis of the use of artificial intelligence in clinical applications (Diagnostic Analytics).

The report provides strategic insights and data on the current status and forecast development of the market and will answer the

following questions:

• Who is developing AI-based diagnostic tools? What is the status of their product development? Do they have regulatory approval?

• What applications are vendors targeting? For each application, what is the value proposition?

• What is the size of the market today and how fast will the market grow?

• What are the major challenges with bringing AI-based tools to market and what strategies are vendors using to overcome them?

• What are the views and perceptions of radiologists regarding AI-based diagnostic tools? Do they see them as an asset or a threat?

Click here or contact Simon Harris for further information about the report.

http://signifyresearch.net/solutions/diagnostic-analytics/

mailto:[email protected]

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5 Reasons Why Radiology Needs Artificial Intelligence