| AI/ML more likely to help | Factors making AI/ML less likely to help |
| Diagnose |
• Improve physician confidence to diagnose a patient concern likely explained by one diagnosis. • Possibly improve diagnosis of rare diseases. • More quickly sifting through and sizing up evidence.
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• Complex interaction of multiple symptoms and possible diseases including psychosocial and social determinant effects that is too much for the computer to manage. • Actual diagnostic computer “nuts and bolts” can be nonsensical. • Diagnoses more accurate in AI/ML demonstrations with well-structured vignettes. • AI/ML diagnoses not likely to include contextual and relational factors.
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| Treat specific diseases |
• Better at judging the chances of what treatment will work best for what patient. ∘ Helping the physician become a better “odds-maker.” ∘ Greater objectivity—more evidentiary basis and less sense of exposure ∘ Readiness to take on complex work that might otherwise have seemed “beyond my bandwidth” when not assisted by AI tools, especially for rarer diseases.
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• Noisy and inaccurate data in EHRs. • May only add real value for rarer diseases. • AI not successful at improving cancer treatment. • Using historical treatment data may not reflect newer approaches. • Many treatment decisions are based on a negotiation with the patient using patient-shared decision making. • Relationships of data elements in existing databases (EHRs, billing data, and so on) often non-linear with Pareto distributions, which makes mathematical predictions challenging.
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| Predict future health events |
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• Predictions may not be actionable. • Existing models may give similar population estimates, but vary widely for individuals. • AI/ML predictions may not improve upon existing tools.
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| Decrease administrative burden |
• Getting through all those letters and forms with less burden. • For U.S. physicians mostly, decrease documentation time and burden.
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• Patient symptoms in primary care are often vague, and even human scribes do not agree how to document them. • Distinction between typing spoken words onto a clinic note versus increasing understanding.
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