Large language models (LLMs), despite their remarkable progress across various general domains, encounter significant barriers in medicine and healthcare. This field faces unique challenges such as domain-specific terminologies and reasoning over specialized knowledge. To address these issues, we propose a novel Multi-disciplinary Collaboration (MC) framework for the medical domain that leverages LLM-based agents in a role-playing setting that participate in a collaborative multi-round discussion, thereby enhancing LLM proficiency and reasoning capabilities. This training-free framework encompasses five critical steps: gathering domain experts, proposing individual analyses, summarising these analyses into a report, iterating over discussions until a consensus is reached, and ultimately making a decision. Our work focuses on the zero-shot setting, which is applicable in real-world scenarios. Experimental results on nine datasets (MedQA, MedMCQA, PubMedQA, and six subtasks from MMLU) establish that our proposed MC framework excels at mining and harnessing the medical expertise within LLMs, as well as extending its reasoning abilities. Our code can be found at \url{https://github.com/gersteinlab/MedAgents}.

翻译：大型语言模型（LLMs）尽管在多个通用领域取得了显著进展，但在医学和医疗保健领域仍面临重大障碍。该领域存在独特挑战，例如领域特定术语及基于专业知识的推理。为解决这些问题，我们提出一种新颖的医学领域多学科协作（MC）框架，该框架利用基于LLM的角色扮演智能体参与协作性多轮讨论，从而增强LLM的能力与推理水平。这一无需训练的框架包含五个关键步骤：收集领域专家、提出个体分析、将这些分析归纳为报告、迭代讨论直至达成共识，并最终做出决策。本研究聚焦于零样本设置，该设置适用于实际场景。在九个数据集（MedQA、MedMCQA、PubMedQA及MMLU的六个子任务）上的实验结果表明，我们提出的MC框架在挖掘和利用LLM内部的医学专业知识以及扩展其推理能力方面表现卓越。我们的代码可在\url{https://github.com/gersteinlab/MedAgents}获取。