AI in Medical Diagnosis:
AI Prediction vs Human Judgement
Dóra Göndöcs, Viktor Dörfler

Physicians already work withAI in diagnosis.
And this creates many
practical issues…

Tool, Assistant or Peer?
(Role of AI)
Tool“The younger, less experienced dermatologists
might think of AI as a peer colleague, while themost experienced ones said they could look at
AI as a resident assisting them.”

habits ofokusers
Assistant •• Learn
Prepare things to assist physicianPeer• Should not use “AI says…”• Working differently with AI• Double-check idiagnosis• Help to form opinion• Experienced vs novice dermatologists may use AI
differently• Elaborate less confident diagnosis“If AI said it was a melanoma andI thought of it as a naevus or abasalioma, I would go for safety,
and I would still cut it off.”• What’s in AI’s “mind”?

What’s in AI’s mind?
(Explainability)“Probably the longer I use such an AI tooland previously gave me good predictions,the more I could rely on that in the nextcases.”

Trust“The AI system can assist
but can never become theone who makes the finaldiagnosis.”• Processing power• Well-structured tasks• A less experienced/qualified helper• Tool, Assistant or Peer?

Should not use “AI says…”(Responsibility)• Develops over time and experience working with AI +consistency/clinical validity of predictions• May depend on mastery level of physician

Prediction
Judgement
Decision
Action• Not only diagnozing but also communicating
to the patient• Potentially high stakes/risks involved“My patients want totalk and discuss
every little detail...”May changes physician’s approach and processof diagnosis
New mental model• incorporate both human and AI• learn and adapt to each other• work as a team, joint performance

Clinical workflow• use physicians’ tacit knowledge and experience• Seperate prediction from judgement indiagnosis• AI should adopt to physicians’ mastery level andexperience(probabilities, samples, visualization, etc.)• Human-relatable understanding of AI performance,“theory of mind” (thinking about it as a peer withdiagnostic „style”, patterns)“One key factor is knowing that the outcome ofeach diagnosis was looped back into the system,
which could further train the AI system reliability.”Also• human supervision (vigilance/situationawareness) may be an issue when
“automation” is in charge (“automation bias”)

Working differently with AI(New operating model)

Understand • How AI generates prediction for a specific case
• How AI works scientifically, in general (accuracies,error rates, tendencies, weakness, limitations, etc.)• Reducing risk of human errors but keeping
humans in the loop

• Final decision about diagnosis and treatment
plan remains within physicians’ remit (alsoregulatory)• May depend on level of confidence

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be well-• Physicians must interpret any automateddiagnostic results

Example tasks done by AI (and change of the workflow):
Gatheringinfo

Examination&testsAssessment&diagnosisprescreening/
triageinitial checkof all moles

personalized
probabilities
for differential plan
diagnoses“Some moles might cause
surprises, and checking with myeyes or a dermatoscope might leadto a different diagnosis.”

Treatment
plan&commMonitoring&feedbacktrack moleevolutionover time