让AI像医生一样结合病历和知识库做决策
DoctorRAG: Medical RAG Fusing Knowledge with Patient Analogy through Textual Gradients
- 用概念标签+双路检索融合医学知识与相似病例
- 多智能体文本梯度确保回答精准贴合患者情况
- 支持多语言多任务，适合临床辅助系统开发
现有医疗RAG系统主要依赖医学知识库，忽视了来自相似患者病例的隐性经验——这是人类临床推理的关键。为弥补这一差距，我们提出DoctorRAG，一种模拟医生思维的RAG框架，融合显式临床知识与隐式病例经验。通过为查询和知识源分配概念标签，并采用知识与患者双重检索机制，提升召回精度。进一步引入Med-TextGrad模块，利用多智能体文本梯度，确保最终输出严格遵循所检索知识与患者需求。在多语言、多任务数据集上的实验表明，DoctorRAG显著优于主流基线RAG模型，且通过迭代优化持续提升性能。该方法生成的回答更准确、相关且全面，推动构建更接近医生思维的医疗推理系统。
原文摘要 · Abstract (English)
Existing medical RAG systems mainly leverage knowledge from medical knowledge bases, neglecting the crucial role of experiential knowledge derived from similar patient cases -- a key component of human clinical reasoning. To bridge this gap, we propose DoctorRAG, a RAG framework that emulates doctor-like reasoning by integrating both explicit clinical knowledge and implicit case-based experience. DoctorRAG enhances retrieval precision by first allocating conceptual tags for queries and knowledge sources, together with a hybrid retrieval mechanism from both relevant knowledge and patient. In addition, a Med-TextGrad module using multi-agent textual gradients is integrated to ensure that the final output adheres to the retrieved knowledge and patient query. Comprehensive experiments on multilingual, multitask datasets demonstrate that DoctorRAG significantly outperforms strong baseline RAG models and gains improvements from iterative refinements. Our approach generates more accurate, relevant, and comprehensive responses, taking a step towards more doctor-like medical reasoning systems.
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