There are two main barriers to using large language models (LLMs) in clinical reasoning. Firstly, while LLMs exhibit significant promise in Natural Language Processing (NLP) tasks, their performance in complex reasoning and planning falls short of expectations. Secondly, LLMs use uninterpretable methods to make clinical decisions that are fundamentally different from the clinician's cognitive processes. This leads to user distrust. In this paper, we present a multi-agent framework called ArgMed-Agents, which aims to enable LLM-based agents to make explainable clinical decision reasoning through interaction. ArgMed-Agents performs self-argumentation iterations via Argumentation Scheme for Clinical Discussion (a reasoning mechanism for modeling cognitive processes in clinical reasoning), and then constructs the argumentation process as a directed graph representing conflicting relationships. Ultimately, use symbolic solver to identify a series of rational and coherent arguments to support decision. We construct a formal model of ArgMed-Agents and present conjectures for theoretical guarantees. ArgMed-Agents enables LLMs to mimic the process of clinical argumentative reasoning by generating explanations of reasoning in a self-directed manner. The setup experiments show that ArgMed-Agents not only improves accuracy in complex clinical decision reasoning problems compared to other prompt methods, but more importantly, it provides users with decision explanations that increase their confidence.

翻译：在临床推理中应用大型语言模型（LLM）主要面临两大障碍。首先，尽管LLM在自然语言处理（NLP）任务中展现出显著潜力，但其在复杂推理与规划方面的表现仍不及预期。其次，LLM采用不可解释的方法进行临床决策，这与临床医生的认知过程存在本质差异，从而导致用户对其缺乏信任。本文提出一种名为ArgMed-Agents的多智能体框架，旨在通过智能体间的交互实现基于LLM的可解释临床决策推理。ArgMed-Agents通过临床讨论论证方案（一种用于建模临床推理认知过程的推理机制）进行自论证迭代，随后将论证过程构建为表示冲突关系的有向图。最终，系统利用符号求解器识别出一系列合理且连贯的论证以支持决策。我们构建了ArgMed-Agents的形式化模型，并提出理论保证的猜想。ArgMed-Agents使LLM能够通过自主生成推理解释，模拟临床论证推理的过程。实验结果表明，与其他提示方法相比，ArgMed-Agents不仅在复杂临床决策推理问题上提升了准确性，更重要的是能为用户提供增强决策信心的解释说明。