Background: Cognitive biases in clinical decision-making significantly contribute to errors in diagnosis and suboptimal patient outcomes. Addressing these biases presents a formidable challenge in the medical field. This study explores the role of large language models (LLMs) in mitigating these biases through the utilization of a multi-agent framework. We simulate the clinical decision-making processes through multi-agent conversation and evaluate its efficacy in improving diagnostic accuracy. Methods: A total of 16 published and unpublished case reports where cognitive biases have resulted in misdiagnoses were identified from the literature. In the multi-agent system, we leveraged GPT-4 Turbo to facilitate interactions among four simulated agents to replicate clinical team dynamics. Each agent has a distinct role: 1) To make the initial and final diagnosis after considering the discussions, 2) The devil's advocate and correct confirmation and anchoring bias, 3) The tutor and facilitator of the discussion to reduce premature closure bias, and 4) To record and summarize the findings. A total of 80 simulations were evaluated for the accuracy of initial diagnosis, top differential diagnosis and final two differential diagnoses. Findings: In a total of 80 responses evaluating both initial and final diagnoses, the initial diagnosis had an accuracy of 0% (0/80), but following multi-agent discussions, the accuracy for the top differential diagnosis increased to 71.3% (57/80), and for the final two differential diagnoses, to 80.0% (64/80). The system demonstrated an ability to reevaluate and correct misconceptions, even in scenarios with misleading initial investigations. Interpretation: The LLM-driven multi-agent conversation system shows promise in enhancing diagnostic accuracy in diagnostically challenging medical scenarios.

翻译：背景：临床决策中的认知偏差是导致诊断错误及不良患者预后的重要原因。解决这些偏差是医学领域的一项艰巨挑战。本研究探讨了大型语言模型（LLMs）通过多智能体框架在减轻此类偏差中的作用。我们通过多智能体对话模拟临床决策过程，并评估其在提升诊断准确性方面的效能。方法：从文献中筛选出16例已发表及未发表的因认知偏差导致误诊的病例报告。在多智能体系统中，我们利用GPT-4 Turbo促进四个模拟智能体之间的交互，以复现临床团队动态。每个智能体承担特定角色：1）在讨论后做出初步及最终诊断；2）充当“魔鬼代言人”，纠正确认偏差与锚定偏差；3）作为讨论引导者与协调员，减少过早闭合偏差；4）记录并总结发现。共对80次模拟进行了评估，涵盖初步诊断、首要鉴别诊断及最终两个鉴别诊断的准确性。结果：在80次评估初步及最终诊断的响应中，初步诊断准确率为0%（0/80），但经多智能体讨论后，首要鉴别诊断准确率提升至71.3%（57/80），最终两个鉴别诊断准确率提升至80.0%（64/80）。该模型在初始检查具有误导性的情境下，仍表现出重新评估并纠正错误认知的能力。解释：由LLM驱动的多智能体对话系统在提升诊断困难的医疗场景中的准确性方面展现出潜力。