We introduce RJUA-QA, a novel medical dataset for question answering (QA) and reasoning with clinical evidence, contributing to bridge the gap between general large language models (LLMs) and medical-specific LLM applications. RJUA-QA is derived from realistic clinical scenarios and aims to facilitate LLMs in generating reliable diagnostic and advice. The dataset contains 2,132 curated Question-Context-Answer pairs, corresponding about 25,000 diagnostic records and clinical cases. The dataset covers 67 common urological disease categories, where the disease coverage exceeds 97.6\% of the population seeking medical services in urology. Each data instance in RJUA-QA comprises: (1) a question mirroring real patient to inquiry about clinical symptoms and medical conditions, (2) a context including comprehensive expert knowledge, serving as a reference for medical examination and diagnosis, (3) a doctor response offering the diagnostic conclusion and suggested examination guidance, (4) a diagnosed clinical disease as the recommended diagnostic outcome, and (5) clinical advice providing recommendations for medical examination. RJUA-QA is the first medical QA dataset for clinical reasoning over the patient inquiries, where expert-level knowledge and experience are required for yielding diagnostic conclusions and medical examination advice. A comprehensive evaluation is conducted to evaluate the performance of both medical-specific and general LLMs on the RJUA-QA dataset. Our data is are publicly available at \url{https://github.com/alipay/RJU_Ant_QA}.

翻译：我们提出RJUA-QA，一个新颖的医学问答与临床循证推理数据集，旨在弥合通用大语言模型与医学专用大语言模型应用之间的差距。RJUA-QA源自真实临床场景，旨在促进大语言模型生成可靠的诊断与建议。该数据集包含2,132个精心构建的“问题-上下文-答案”三元组，对应约25,000份诊断记录与临床病例。数据集覆盖67种常见泌尿系统疾病类别，疾病覆盖率超过泌尿外科就诊人群的97.6%。RJUA-QA中的每条数据实例包含：（1）反映真实患者对临床症状与医疗状况咨询的问题；（2）包含综合专家知识的上下文，作为医学检查与诊断的参考；（3）提供诊断结论与建议检查指导的医生回复；（4）作为推荐诊断结果的临床确诊疾病；（5）提供医学检查建议的临床指导意见。RJUA-QA是首个面向患者问诊的临床推理医学问答数据集，其诊断结论与医学检查建议的生成需要专家级知识与经验。我们基于RJUA-QA数据集对医学专用与通用大语言模型进行了全面性能评估。本数据集公开访问地址为：\url{https://github.com/alipay/RJU_Ant_QA}。