Abstract
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 this https URL.
Abstract (translated)
基础模型在推动医疗领域方面已经变得非常有价值。然而，在实现复杂医疗任务的LLM的有效部署仍是一个开放问题。我们的新框架，医疗决策代理（MDAgents），旨在通过自动分配LLM的有效合作结构来解决这个空白。分配独奏或团体合作结构是根据当前医疗任务的复杂程度来定制的，模拟真实世界医学决策过程。我们用最先进的LLM在一系列具有挑战性的医疗基准中评估我们的框架和基线方法：MedQA，MedMCQA，PubMedQA，DDXPlus，PMC-VQA，Path-VQA和MedVidQA，在需要理解多模态医学推理的5个基准中实现了最佳性能。消融研究揭示了MDAgents在适应协作代理数量以优化效率和精度方面的优势，展示了其在复杂临床团队动态中的稳健性。我们还研究了群体共识的动态，提供了关于协作代理在复杂临床团队中的行为的一些见解。代码可以在这个链接中找到。
URL
https://arxiv.org/abs/2404.15155
