A Structured Debate-Mixture-of-Agents Framework for Complex Clinical Diagnostic Decision Support

2026-09-04

AI Summary

本文提出了一种名为DMoA的多智能体框架，通过结构化角色互动支持迭代诊断推理，解决了复杂临床诊断中大型语言模型局限性的问题。

Abstract

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.

Problem

• Large language models
• medical tasks
• clinical diagnosis
• complex diagnostic settings

Innovation

• Debate-Mixture-of-Agents
• multi-agent framework
• iterative diagnostic reasoning
• structured workflow