通过结构化诊断推理数据和强化学习增强临床诊断探究
Dr. Assistant: Enhancing Clinical Diagnostic Inquiry via Structured Diagnostic Reasoning Data and Reinforcement Learning
AI总结：
Dr. Assistant通过结构化诊断推理数据和强化学习提升临床诊断能力，优于开放源代码模型并接近闭源模型表现。
摘要：
临床决策支持系统（CDSSs）为医生提供推理和探究指导，但面临显著挑战，包括维护成本高和泛化能力差。最近，大语言模型（LLMs）因其广泛的知识储备、检索和沟通能力被广泛应用于医疗领域。尽管LLMs表现出色并在医学基准测试中表现优异，但其诊断推理和探究能力受限。为缓解这一问题，我们提出（1）临床诊断推理数据（CDRD）结构以捕捉抽象的临床推理逻辑及其构建流程，以及（2）Dr. Assistant，一个具备临床推理和探究能力的临床诊断模型。其训练过程包括两个阶段：先进行有监督训练（SFT），随后使用定制的奖励函数进行强化学习（RL）。我们还引入了一个基准测试来评估诊断推理和探究能力。我们的实验表明，Dr. Assistant在开放源代码模型上表现更优，并在闭源模型上达到竞争性表现，为临床诊断探究指导提供了一种有效解决方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
