Foundation models have become invaluable in advancing the medical field. Despite their promise, the strategic deployment of LLMs for effective utility in complex medical tasks remains an open question. Our novel framework, Medical Decision-making Agents (MDAgents) aims to address this gap by automatically assigning the effective collaboration structure for LLMs. Assigned solo or group collaboration structure is tailored to the complexity of the medical task at hand, emulating real-world medical decision making processes. We evaluate our framework and baseline methods with state-of-the-art LLMs across a suite of challenging medical benchmarks: MedQA, MedMCQA, PubMedQA, DDXPlus, PMC-VQA, Path-VQA, and MedVidQA, achieving the best performance in 5 out of 7 benchmarks that require an understanding of multi-modal medical reasoning. Ablation studies reveal that MDAgents excels in adapting the number of collaborating agents to optimize efficiency and accuracy, showcasing its robustness in diverse scenarios. We also explore the dynamics of group consensus, offering insights into how collaborative agents could behave in complex clinical team dynamics. Our code can be found at https://github.com/mitmedialab/MDAgents.

通过自动分配适合医学任务复杂性的个体或群体协作结构，模拟真实世界医学决策过程，我们的新颖框架Medical Decision-making Agents（MDAgents）在一系列具有挑战性的医学基准测试中评估了我们的框架和基准方法，通过最优性能在5个需要多模态医学推理的7个基准测试中取得了最佳表现，消融研究揭示了MDAgents在适应协作代理数量以优化效率和准确性方面的卓越性能，并探索了群体共识的动态，提供了协作代理在复杂临床团队动态中的行为洞察。

医疗决策中的适应性协作策略研究