用结构化推理数据和强化学习训练医生助手，提升临床诊断能力。
Dr. Assistant: Enhancing Clinical Diagnostic Inquiry via Structured Diagnostic Reasoning Data and Reinforcement Learning
- 构建临床推理数据结构，系统化捕捉诊断逻辑
- 两阶段训练使模型诊断准确率超越开源模型，媲美闭源模型
- 适合医疗AI研发者与临床辅助系统开发者参考
临床决策支持系统（CDSS）在提供诊断推理与问询指导方面面临维护成本高、泛化能力差的问题。尽管大语言模型（LLMs）凭借其知识储备、信息检索与沟通能力在医疗领域广泛应用，但其诊断推理与问询能力仍受限。为此，本文提出：(1) 临床诊断推理数据（CDRD）结构及其构建流程，用于捕捉抽象的临床推理逻辑；(2) Dr. Assistant 模型，具备临床推理与问询能力。该模型采用两阶段训练：先进行监督微调（SFT），再通过定制奖励函数进行强化学习（RL）。同时，我们构建了一个基准测试集，用于评估诊断推理与问询表现。实验表明，Dr. Assistant 在多个指标上优于开源模型，并达到与闭源模型相当的性能，为临床诊断问询指导提供了有效解决方案。项目详情见：https://github.com/YGswu/Dr.-Assistant。
原文摘要 · Abstract (English)
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. Project information can be found at: https://github.com/YGswu/Dr.-Assistant .
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