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AI and Value in Radiology Unlimited Horizons

Richard Tuft MB, BS, FRCS, FRCR, FCRad (Diag) SAExecutive Director Radiological Society of South Africa

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Acknowledgements

Dr Kuben Naidu RSSA President

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What is AI ?AI refers to the branch of computer science which develops computer algorithms to perform tasksusually associated with human intelligence.

Provides computer with knowledge without beingspecifically programmed. Assisted by human interventionand ‘labelling’. Improves with more data.

A subset of machine learning where the machine learns the process required to classify data

A subset of representational learning but uses multiple processing layers and ‘neural networks’

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Where next?

?

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Artificial Intelligence

DisruptiveTransformativeExponentialExciting

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What is the future of radiology and radiologists and radiographers?

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Already sorted?MCkinsey

Hospital Group

Managers

Doctors

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No More Radiologists

Reduced cost? Increased quality? Value to patient?

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An eclectic mix?

• Looney Tunes

• Wile E Coyote

• Roadrunner

• Geoffrey Hinton

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There is more to Radiology• Computer scientist’s view

• Stop training

• Too many

• Deep leaning will do it better

• Professional considerations,

• all professions

• Ethical considerations.

• Medico-legal aspects

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Radiology evolves

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Radiologist evolves

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PACS Picture Archiving and Communication System

Teleradiology

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Teleradiology

• Subset of telemedicine

• All radiology is teleradiology

• Licensing and accreditation

• Quality control

• Data protection

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Increase in data

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Increase in Data and workloadRadiologist ‘Burnout’Report Quality

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Where’s Wally?

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What do radiologists do?

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What do radiologists do?

• Image Interpretation

• Public/Academic 36%

• Private 55-60%

• Interventional 10%

4%6%

4%

12%

15%

12% 11%

36%

Interpretation ProceduresConsultations TeachingPersonal ProtocolsCME Research

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Too many radiologists?

SA Total 1.2 SA Private 4.7

USA 11

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How will AI affect radiology?

• Workflow

• Image interpretation

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Radiology workflow AI Imaging Value Chain

Patient referral Clinical Decision support

Imaging modality

Operation Dose Optimisation

Scheduling Staffing

Imaging protocol selection

Worklist priority Hanging protocols

Previous studies EHR

Reporting Interpretation

Patient and Referrer CommunicationAppropriateness

Utilisation Billing

AI

Value

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Radiology Guidelines iGuide Clinical Decision Support

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Elements of a good referral

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Clinical Decision Support CDS

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ESR iGuide pilot project in Sweden

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AI and CDS

• “AI coupled with CDS will improve the decision process and thereby optimise clinical and radiological workflow.”

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Image Transformation

• Transforms noisy input image from low dose CT scan to less noisy image

• Input image fed into neural network and mapped into encode representation to create output image.

• Neural network has to be trained on low and routine dose CT to learn mapping between two.

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Image Transformation

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Workflow Triage and Prioritisation

• Detection of intracranial haemorrhage.

• Algorithm processes exam and generates binary output (positive or negative)

• If positive, priority upgraded on radiologists worklist

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Automated Hanging Protocols

• Radiologist opens study

• Machine learning creates optimal hanging protocol

• Radiologist reports and sends to RIS and or EHR

PACS

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Image Interpretation and Reporting Ongoing AI Imaging Applications

• Image segmentation

• Image interpretation

Neuro Chest Abdomen MSK Breast other

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Publications on Computer Aided Diagnosis

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TB

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Multiple Abnormalities

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Lung Nodules

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AI increases the accuracy and efficiency of neuroradiologists by:

• Detecting subtle changes invisible to human eye

• Processing huge datasets

• Characterising rare brain tumours

• Quantifying abnormalities

• Monitoring disease evolution and response to treatment

• Unravelling relationships in population studies

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Where does AI sit in a radiology department?

• Multiple vendors

• Different areas of expertise

• No single platform

• Integration with PACS/RIS/HIS

• Business as usual

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Ethics of AI Principles

• Safety

• Avoidance of bias

• Transparency and explainability

• Privacy and protection of data

•Decision making on diagnosis and treatment

• Liability for decisions made

• Application of human values

•Governance

•

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Liability

• Poor outcomes

• Radiologist

• Hospital or Institution

• Software Developer

• AI Vendor.

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AI for reporting• Semi-automisation of reporting

• Automatic inclusion of quantitative data/radiomics

• Automatic scoring (PIRADS, BIRADS, LIRADS etc)

• Automatic adaption of style and language

• Missing topics check list

• Automated methods for labelling and coding of findings, inclusion of Common Data Elements.

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Radiologist training

Education years

University Medical School

Specialist training Practice

12-15 years 7 years 5 years

18 25 30Age

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Arguments for AI Replacing Radiologists

• Faster and cheaper, no reason to pay radiologists

• Marked reduction in size of workforce 30-50%?

• Reduce cost of radiology

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Arguments against AI replacing radiologists

• Algorithms will never be able to interpret images as well as humans?

• Radiologists do more than just interpreting images

• If interpretation goes, radiologist will focus on other aspects

• Regulators would not allow decisions to be made by machines without supervision

• Legal liability

• Patients would never agree

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Likely scenario 5-10 years

• AI will allow optimal utilisation of human resources and equipment

• Radiology will transform from a subjective perceptive skill into an objective science

• AI will reduce workload by taking out normals.

• Triaged intervention will improve acute care and intervention

• Integration of radiomics, genomics and proteomics

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With limited resources

• SA has very few radiologists

• Work currently not reported will be

• Shortage of radiologists will be alleviated

• Divergent outcomes will be normalised

SA

31

1.2

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The radiologist and radiology will change

• Radiologists must be integral in the development of algorithms

• The radiologist will be come a validator, adjudicator and communicator

• New skills needed

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Threats to SA Radiology

• Scarce resource

• Reduction in trainees

• NHI

• Corporatisation

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Corporatisation of Radiology• Hospital ownership of radiology

• Launched via press

• Legal and ethical obstacles

• International models not suitable for SA.

• Threatening, coercive, leases run down

• Revenue shifting

• Cost increases

• Loss of scare resources.

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–Stephen Hawking 2018

“Our future is a race between the growing power of our technology and the wisdom with which we use it.”

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–Felix Nensa

“AI will not replace radiologists but radiologists who do not use AI will be replaced by those who do”

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Thankyou