Using ChatGPT for Differential Diagnosis

Much of the evidence comes from case vignettes. In a 2023 JAMA research letter, Kanjee and colleagues tested GPT-4 on 70 difficult cases from the New England Journal of Medicine clinicopathological conferences. The final diagnosis appeared in the model's differential in about 64 percent of cases and was its top diagnosis in about 39 percent. A randomized trial by Goh and colleagues, published in JAMA Network Open in 2024, gave 50 physicians either GPT-4 plus usual resources or usual resources alone for structured diagnostic cases. Access to GPT-4 did not significantly improve physicians' diagnostic reasoning scores. GPT-4 working alone, however, scored substantially higher than the physicians who used only usual resources. One interpretation is that physicians did not know how to use the tool well, or did not trust it when it disagreed with them. Research systems such as Google's AMIE have also been tested on diagnostic dialogue and differential generation, with strong results in controlled studies. These results need careful reading. Published case series may be in a model's training data. Vignettes arrive already cleaned up, with the relevant findings chosen and summarized, while real patients bring incomplete, contradictory and unstated information. Scoring "correct diagnosis somewhere in the list" rewards long lists, which can prompt extra testing. The common misconception is that benchmark accuracy equals bedside accuracy. It does not. Known failure modes include confident but wrong reasoning, invented references or lab thresholds, anchoring on whatever diagnosis the user hints at, over-weighting common textbook presentations, and missing time-critical conditions when the description leaves out vital signs. Privacy is a separate issue. Entering identifiable patient information into a consumer chatbot without a business associate agreement can breach HIPAA, so clinicians should use tools their organization has approved.