This document discusses 5 reasons why radiology needs artificial intelligence: 1. There is a global shortage of radiologists that is expected to worsen as imaging volumes increase faster than new radiologists enter the field. AI can help improve radiologist productivity. 2. AI can enhance radiologist productivity through functions like smart alerts, automatic image annotation, and faster access to patient information. 3. AI can help improve diagnostic accuracy by reducing human error, tracking lesions over time more quantitatively, and providing automated second opinions. 4. AI may help lower misdiagnosis rates by alerting radiologists to disease indicators and comparing new cases to existing ones to reduce fatigue-related errors. 5. AI could help improve

1. 5 Reasons Why Radiology Needs
Artificial Intelligence
Created on October 25th, 2016
Simon Harris, Managing Director & Principal Analyst
simon.harris@signifyresearch.net

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Global shortage of radiologists1
5 Reasons why radiology needs artificial intelligence
Enhanced productivity2
Better diagnostic accuracy3
Lower rates of misdiagnosis4
Improved patient outcomes5
Background
The 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.

3. Reason 1 - Shortage of radiologists in many countries
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US
105
DE
98
UK
49
ES
112
NG
2
BR
21
ZA
17
SA
35
AU
86
JP
46
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 population
Sources: National radiology societies and government agencies. See slide 9 for details

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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

5. Reason 3 - Better diagnostic accuracy
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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

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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 prioritised
Review scans in real-time and automatically escalate priority
cases 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

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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

9. References
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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 Benchmark
2. Jena AB, Seabury S, Lakdawalla D, Chandra A. Malpractice risk according to physician specialty. N Engl J Med 2011;365(7):629–636. CrossRef, Medline
3. American Cancer Society
4. 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.

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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 Harris
Managing 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.
simon.harris@signifyresearch.net
+44 1234 436 150
Diagnostic Analytics - World Market Report – 2016 Edition
Signify 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.