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OBJECTIVE: This study evaluates ChatGPT's symptom-checking accuracy across a broad range of diseases using the Mayo Clinic Symptom Checker patient service as a benchmark. METHODS: We prompted ChatGPT with symptoms of 194 distinct diseases. By comparing its predictions with expectations, we calculated a relative comparative score (RCS) to gauge accuracy. RESULTS: ChatGPT's GPT-4 model achieved an average RCS of 78.8%, outperforming the GPT-3.5-turbo by 10.5%. Some specialties scored above 90%. DISCUSSION: The test set, although extensive, was not exhaustive. Future studies should include a more comprehensive disease spectrum. CONCLUSION: ChatGPT exhibits high accuracy in symptom checking for a broad range of diseases, showcasing its potential as a medical training tool in learning health systems to enhance care quality and address health disparities.
Chen et al. (Mon,) studied this question.