Because top AI models score well on medical knowledge benchmarks, they can be trusted as diagnostic assistants to help narrow down what a patient's symptoms might be.
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Expert: Arya Rao (Harvard Medical School); Dr. Marc Succi (Massachusetts General Hospital), JAMA Network Open study authors A JAMA Network Open study led by Harvard medical student Arya Rao tested 21 leading off-the-shelf AI models on 29 standardized clinical vignettes. With a full portfolio of medical information and a final-diagnosis request, leading models were correct 91% of the time - but in early differential diagnosis, where clinicians rule out conditions while weighing possibilities under uncertainty, more than 8 out of 10 cases failed. 'Every model we tested failed on the vast majority of cases,' Rao said. Coauthor Dr. Marc Succi (Massachusetts General Hospital radiologist) warns today's off-the-shelf LLMs 'should not be trusted for patient-facing diagnostic reasoning without structured comprehensive human review.' Raw accuracy per case ranged 63-78%, meaning models were often partially correct - which the authors say is itself risky because wrong differentials cause delays in care, unnecessary procedures and higher costs. Source: https://www.theregister.com/software/2026/04/15/llms-fail-in-8-out-of-10-early-differential-diagnosis-cases/5227208
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