Important Paper: Why almost all ML models for medicine are wrong-and what we need for evidence-based medical AI

data analysis
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My LinkedIn feed mentioned this recent paper by Italian researchers on the unreliability “AI” being used in medical decision making. Many of the points should be familiar to Data Methods participants, but it is nice to see others sounding the alarm.

Cabitza, F., Jurman, G., Molinari, F., & Bellazzi, R. (2026). Why almost all ML models for medicine are wrong-and what we need for evidence-based medical AI. International Journal of Medical Informatics, 106538. https://www.sciencedirect.com/science/article/pii/S1386505626002789?via%3Dihub

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This paper really captures the key challenges of (blindly) applying modern ML methods. Good read. One aspect I’d like to underscore is that these challenges do not cease to exist in different fields: they are largely universal across fields so long as they operate in a noisy data/measurement error world.

At this time, there is barely any critique as soon as we move beyond the medical/health space, and I hope this changes.

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