Chatbots may be able to pass medical exams, but that doesn’t mean they make good doctors, according to a new, large-scale study of how people get medical advice from large language models.

The controlled study of 1,298 UK-based participants, published today in Nature Medicine from the Oxford Internet Institute and the Nuffield Department of Primary Care Health Sciences at the University of Oxford, tested whether LLMs could help people identify underlying conditions and suggest useful courses of action, like going to the hospital or seeking treatment. Participants were randomly assigned an LLM — GPT-4o, Llama 3, and Cohere’s Command R+ — or were told to use a source of their choice to “make decisions about a medical scenario as though they had encountered it at home,” according to the study. The scenarios included ailments like “a young man developing a severe headache after a night out with friends for example, to a new mother feeling constantly out of breath and exhausted,” the researchers said.

When the researchers tested the LLMs without involving users by providing the models with the full text of each clinical scenario, the models correctly identified conditions in 94.9 percent of cases. But when talking to the participants about those same conditions, the LLMs identified relevant conditions in fewer than 34.5 percent of cases. People didn’t know what information the chatbots needed, and in some scenarios, the chatbots provided multiple diagnoses and courses of action. Knowing what questions to ask a patient and what information might be withheld or missing during an examination are nuanced skills that make great human physicians; based on this study, chatbots can’t reliably replicate that kind of care.

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