用知识图谱和智能提问提升大模型的临床诊断能力
MedKGI: Iterative Differential Diagnosis with Medical Knowledge Graphs and Information-Guided Inquiring
- 结合医学知识图谱约束推理，避免生成虚假医疗信息
- 根据信息增益选择关键问题，对话效率提升30%
- 结构化状态追踪证据，保持多轮对话逻辑一致
大型语言模型在临床诊断中展现出巨大潜力，但现有模型难以模拟真实诊疗中的迭代式、假设驱动推理。主要存在三大问题：（1）因缺乏权威知识支撑而产生幻觉内容；（2）提出冗余或低效问题，阻碍诊断进展；（3）多轮对话中失去连贯性，导致结论矛盾。为此，我们提出MedKGI框架，融合医学知识图谱以约束推理至可验证的医学本体，基于信息增益选择具有区分性的提问策略，并采用OSCE格式的结构化状态维护多轮证据一致性。在临床基准测试中，MedKGI在诊断准确率与提问效率上均优于主流大模型基线，平均对话效率提升30%，同时保持最先进准确率。
原文摘要 · Abstract (English)
Recent advancements in Large Language Models (LLMs) have demonstrated significant promise in clinical diagnosis. However, current models struggle to emulate the iterative, diagnostic hypothesis-driven reasoning of real clinical scenarios. Specifically, current LLMs suffer from three critical limitations: (1) generating hallucinated medical content due to weak grounding in verified knowledge, (2) asking redundant or inefficient questions rather than discriminative ones that hinder diagnostic progress, and (3) losing coherence over multi-turn dialogues, leading to contradictory or inconsistent conclusions. To address these challenges, we propose MedKGI, a diagnostic framework grounded in clinical practices. MedKGI integrates a medical knowledge graph (KG) to constrain reasoning to validated medical ontologies, selects questions based on information gain to maximize diagnostic efficiency, and adopts an OSCE-format structured state to maintain consistent evidence tracking across turns. Experiments on clinical benchmarks show that MedKGI outperforms strong LLM baselines in both diagnostic accuracy and inquiry efficiency, improving dialogue efficiency by 30% on average while maintaining state-of-the-art accuracy.
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