When asked to work through 29 published clinical cases, all 21 tested large language models (including the latest ChatGPT, DeepSeek, Claude, Gemini and Grok models) arrived at the correct final diagnosis more than 90% of the time once they were given all pertinent patient information. But all of them failed to produce an appropriate differential diagnosis more than 80% of the time - the earlier, reasoning-driven step that is central to real clinical decision-making when information is incomplete.
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Expert: Marc Succi, MD and Arya Rao, Executive Director, MESH Incubator, Mass General Brigham; Lead Author and MD-PhD Student, Harvard Medical School Mass General Brigham researchers tested 21 general-purpose LLMs on 29 published clinical cases, feeding information gradually the way a real case unfolds. While the models named the correct final diagnosis more than 90% of the time once the data was complete, they failed to produce an appropriate differential diagnosis more than 80% of the time - the early, reasoning-driven step that matters most when information is lacking. 'Despite continued improvements, off-the-shelf large language models are not ready for unsupervised clinical-grade deployment,' said corresponding author Marc Succi, MD. 'Differential diagnoses are central to clinical reasoning and underlie the art of medicine that AI cannot currently replicate.' Lead author Arya Rao added: 'These models are great at naming a final diagnosis once the data is complete, but they struggle at the open-ended start of a case, when there isn't much information.' The study, published in JAMA Network Open, also introduced PrIME-LLM, a benchmark that evaluates models across stages of clinical reasoning rather than averaging them; scores ranged from 64% (Gemini 1.5 Flash) to 78% (Grok 4 and GPT-5). The researchers concluded AI should augment, not replace, physician reasoning - with a human in the loop. Source: https://www.massgeneralbrigham.org/en/about/newsroom/press-releases/ai-chatbot-lacks-clinical-reasoning
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