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METHODS: First, a set of evaluation criteria is designed based on acomprehensive literature review. Second, existing candidate criteria areoptimized for using a Delphi method by five experts in medicine andengineering. Third, three clinical experts design a set of medical datasets tointeract with LLMs. Finally, benchmarking experiments are conducted on thedatasets. The responses generated by chatbots based on LLMs are recorded forblind evaluations by five licensed medical experts. RESULTS: The obtainedevaluation criteria cover medical professional capabilities, socialcomprehensive capabilities, contextual capabilities, and computationalrobustness, with sixteen detailed indicators. The medical datasets includetwenty-seven medical dialogues and seven case reports in Chinese. Threechatbots are evaluated, ChatGPT by OpenAI, ERNIE Bot by Baidu Inc., and DoctorPuJiang (Dr. PJ) by Shanghai Artificial Intelligence Laboratory. Experimentalresults show that Dr. PJ outperforms ChatGPT and ERNIE Bot in bothmultiple-turn medical dialogue and case report scenarios.

MedGPTEval: A Dataset and Benchmark to Evaluate Responses of Large Language Models in Medicine