用多智能体树状推理提升复杂医学诊断准确性
Tree-of-Reasoning: Towards Complex Medical Diagnosis via Multi-Agent Reasoning with Evidence Tree
- 构建树形结构记录推理路径与临床证据
- 跨验证机制确保多智能体决策一致性
- 适用于需要深度推理的复杂医学诊断
大语言模型在医疗领域展现出巨大潜力，但在面对真实世界中的复杂医学诊断任务时仍存在不足。主要原因是缺乏足够的推理深度，导致处理大量专业医疗数据时出现信息丢失或逻辑跳跃，引发诊断错误。为此，我们提出一种名为树状推理（Tree-of-Reasoning, ToR）的新颖多智能体框架，通过引入树形结构清晰记录大语言模型的推理路径及对应临床证据，并设计交叉验证机制确保多智能体决策的一致性，从而提升多智能体在复杂医疗场景下的临床推理能力。在真实医疗数据上的实验结果表明，该框架性能优于现有基线方法。
原文摘要 · Abstract (English)
Large language models (LLMs) have shown great potential in the medical domain. However, existing models still fall short when faced with complex medical diagnosis task in the real world. This is mainly because they lack sufficient reasoning depth, which leads to information loss or logical jumps when processing a large amount of specialized medical data, leading to diagnostic errors. To address these challenges, we propose Tree-of-Reasoning (ToR), a novel multi-agent framework designed to handle complex scenarios. Specifically, ToR introduces a tree structure that can clearly record the reasoning path of LLMs and the corresponding clinical evidence. At the same time, we propose a cross-validation mechanism to ensure the consistency of multi-agent decision-making, thereby improving the clinical reasoning ability of multi-agents in complex medical scenarios. Experimental results on real-world medical data show that our framework can achieve better performance than existing baseline methods.
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