Clinical Decision Support Systems (CDSSs) provide reasoning and inquiry guidance for physicians, yet they face notable challenges, including high maintenance costs and low generalization capability. Recently, Large Language Models (LLMs) have been widely adopted in healthcare due to their extensive knowledge reserves, retrieval, and communication capabilities. While LLMs show promise and excel at medical benchmarks, their diagnostic reasoning and inquiry skills are constrained. To mitigate this issue, we propose (1) Clinical Diagnostic Reasoning Data (CDRD) structure to capture abstract clinical reasoning logic, and a pipeline for its construction, and (2) the Dr. Assistant, a clinical diagnostic model equipped with clinical reasoning and inquiry skills. Its training involves a two-stage process: SFT, followed by RL with a tailored reward function. We also introduce a benchmark to evaluate both diagnostic reasoning and inquiry. Our experiments demonstrate that the Dr. Assistant outperforms open-source models and achieves competitive performance to closed-source models, providing an effective solution for clinical diagnostic inquiry guidance.

翻译：临床决策支持系统（CDSS）为医生提供推理与问询指导，但仍面临维护成本高、泛化能力低等显著挑战。近年来，大型语言模型（LLMs）凭借其丰富的知识储备、检索与交流能力，在医疗领域得到广泛应用。尽管LLMs在医学基准测试中展现出潜力并取得优异表现，但其诊断推理与问询能力仍受局限。为缓解这一问题，我们提出：（1）用于捕捉抽象临床推理逻辑的临床诊断推理数据（CDRD）结构及其构建流程；（2）具备临床推理与问询能力的临床诊断模型Dr. Assistant。其训练采用两阶段流程：先进行监督微调（SFT），随后采用定制奖励函数进行强化学习（RL）。我们还引入了一个评估诊断推理与问询能力的基准测试。实验表明，Dr. Assistant在开源模型中表现优异，并与闭源模型达到相当性能，为临床诊断问询指导提供了有效解决方案。