构建真实临床流程数据集，让AI医生能像真人一样动态问诊、逐步决策。
Medchain: Bridging the Gap Between LLM Agents and Clinical Practice with Interactive Sequence
- 基于1.2万例真实病例，设计可交互的序列化诊疗流程
- 新模型在动态问诊中表现优于现有方法，准确率显著提升
- 适合医疗AI研发者、临床决策系统开发者参考
临床决策是医疗实践中的关键环节，但当前人工智能系统仍难以应对真实场景。尽管大语言模型在医学知识测试中表现良好，但在实际诊疗流程中受限于缺乏真实世界数据。为此，我们提出MedChain，一个包含12,163个临床案例的数据集，覆盖临床工作流五大核心阶段。该数据集突出三大特征：个性化、可交互性与序列性。同时，我们构建MedChain-Agent，集成反馈机制与MCase-RAG模块，实现从历史病例中学习并动态调整策略。实验表明，该系统在信息收集与多步任务处理上具备更强适应性，显著超越现有方法。
原文摘要 · Abstract (English)
Clinical decision making (CDM) is a complex, dynamic process crucial to healthcare delivery, yet it remains a significant challenge for artificial intelligence systems. While Large Language Model (LLM)-based agents have been tested on general medical knowledge using licensing exams and knowledge question-answering tasks, their performance in the CDM in real-world scenarios is limited due to the lack of comprehensive testing datasets that mirror actual medical practice. To address this gap, we present MedChain, a dataset of 12,163 clinical cases that covers five key stages of clinical workflow. MedChain distinguishes itself from existing benchmarks with three key features of real-world clinical practice: personalization, interactivity, and sequentiality. Further, to tackle real-world CDM challenges, we also propose MedChain-Agent, an AI system that integrates a feedback mechanism and a MCase-RAG module to learn from previous cases and adapt its responses. MedChain-Agent demonstrates remarkable adaptability in gathering information dynamically and handling sequential clinical tasks, significantly outperforming existing approaches.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。