  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}.
