面向复杂临床诊断决策支持的结构化辩论-混合智能体框架
A Structured Debate-Mixture-of-Agents Framework for Complex Clinical Diagnostic Decision Support
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中文总结 AI 辅助
该研究提出DMoA多智能体框架，在297例罕见病和1719例疑难病例上，较GPT-4o提升诊断准确率10.21个百分点、安全率11.36个百分点，为复杂临床诊断决策提供新方案。
中文摘要 AI 辅助
大型语言模型（LLMs）在医疗任务中展现出潜力，但其单轮问答形式与实际临床诊断流程不符，导致在复杂诊断场景中表现受限。我们开发了Debate-Mixture-of-Agents（DMoA）这一新型多智能体框架，该框架通过结构化的角色交互支持迭代诊断推理。我们在297例罕见病病例和1719例疑难病例上对基础模型和DMoA进行了评估，在两个数据集上，DMoA较GPT-4o基线的最可能诊断准确率提升了10.21个百分点，安全率提升了11.36个百分点。消融实验显示，性能提升并非仅源于使用更多模型或更长输出，还反映了结构化工作流的贡献。进一步分析考察了框架设计、基础模型选择和令牌预算对性能的影响，结果表明DMoA在4*2结构、更强的基础模型和更大令牌预算下表现更优。这些发现证明了DMoA在临床任务中的潜力，并提示需进一步研究多智能体框架。
英文摘要
Large language models (LLMs) show potential for medical tasks, but their single-turn question-answer format does not reflect how clinical diagnosis is performed in practice. As a result, they remain limited in complex diagnostic settings. We developed Debate-Mixture-of-Agents (DMoA), a novel multi-agent framework that structures role-based interaction to support iterative diagnostic reasoning. Base models and DMoA were evaluated on 297 rare disease cases and 1,719 challenging cases. Across both datasets, DMoA improved most likely diagnosis accuracy by 10.21 percentage points and safety rate by 11.36 percentage points over GPT-4o baseline. Ablation experiments showed that the gains were not simply due to the use of more models or longer outputs, but also reflected the contribution of the structured workflow. Further analyses examined how framework design, base model choice, and token budget affected performance. DMoA performed better with a 4*2 structure, stronger base models, and a larger token budget. These findings demonstrate the potential of DMoA for clinical tasks and suggest further investigation of multi-agent frameworks.
发表机构
- College of Computer Science, Sichuan University（四川大学计算机学院）
- West China Hospital, Sichuan University（四川大学华西医院）
- Med-X Center for Informatics, Sichuan University（四川大学华西医学信息学中心）
机构由 AI 辅助整理，请以论文原文为准。
