  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 MedAgents, a novel multi-disciplinary collaboration
framework for the medical domain. MedAgents 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 MedAgents framework excels at mining and harnessing the medical
expertise within LLMs, as well as extending its reasoning abilities. Our code
can be found at https://github.com/gersteinlab/MedAgents.
