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 Decision (a reasoning mechanism for modeling cognitive processes in clinical reasoning), and then constructs the argumentation process as a directed graph representing conflicting relationships. Ultimately, Reasoner(a symbolic solver) identify a series of rational and coherent arguments to support decision. 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：摘要: 将大语言模型应用于临床推理面临两大主要障碍。其一，尽管大语言模型在自然语言处理任务中展现出显著潜力，但其在复杂推理与规划中的表现未达预期；其二，大语言模型采用与临床医生认知过程本质不同的不可解释方法进行临床决策，导致用户信任度降低。本文提出名为ArgMed-Agents的多智能体框架，旨在通过交互使基于大语言模型的智能体实现可解释的临床决策推理。该框架通过临床决策论辩模式（一种模拟临床推理认知过程的推理机制）进行自我论辩迭代，然后将论辩过程构建为表示冲突关系的有向图，最终由符号求解器Reasoner识别出一系列理性且连贯的论据以支撑决策。ArgMed-Agents使大语言模型通过自主生成推理解释的方式，模拟临床论证推理过程。实验表明，相较于其他提示方法，ArgMed-Agents不仅提升了复杂临床决策推理问题的准确性，更重要的是能为用户提供增强其信心的决策解释。