MedKGI: 基于医学知识图谱和信息引导提问的迭代诊断
MedKGI: Iterative Differential Diagnosis with Medical Knowledge Graphs and Information-Guided Inquiring
- Sichuan University（四川大学）
- HKUST（香港科技大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
MedKGI通过整合医学知识图谱和信息引导提问，提升临床诊断的准确性和效率。
AI中文摘要：
近年来，大型语言模型（LLMs）在临床诊断中展现出显著潜力。然而，当前模型难以模拟真实临床场景中迭代、基于诊断假设的推理过程。具体而言，当前LLMs存在三个关键局限：（1）由于对验证知识的弱 grounding，生成幻觉的医疗内容；（2）提问冗余或低效，而非具有区分性的提问，阻碍诊断进展；（3）在多轮对话中失去连贯性，导致矛盾或不一致的结论。为解决这些挑战，我们提出了MedKGI，一种基于临床实践的诊断框架。MedKGI整合医学知识图谱（KG）以约束推理至验证的医学本体，基于信息增益选择问题以最大化诊断效率，并采用OSCE格式的结构化状态以在多轮对话中保持一致的证据追踪。在临床基准测试中，MedKGI在诊断准确性和询问效率方面均优于强大的LLM基线，平均提高对话效率30%，同时保持最先进的准确性。
英文摘要：
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.
