Benchmarking the symptom-checking capabilities of ChatGPT for a broad range of diseases
Anjun Chen, Drake O Chen, Lü Tian
Abstract
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.