MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning

By Xiangru Tang, Anni Zou, Zhuosheng Zhang, Yilun Zhao, Xingyao Zhang, Arman Cohan, Mark B. Gerstein. Annual Meeting of the Association for Computational Linguistics, 2023

457 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Health Informatics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/44d16a076c00ecada3d425203377e4ec951c4ed0

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Nitesh V. Chawla

Citations in the corpus

Large Language Model based Multi-Agents: A Survey of Progress and Challenges (2024, methodology): “[Tang et al. , 2023] also utilizes multiple LLM-based agents as distinct domain experts to do the collaborative discussion on a medical report to reach a consensus for medical diagnosis.”
The rise and potential of large language model based agents: a survey (2025, unknown): “In MedAgents [550], the authors enhance the reasoning ability of LLMs in the medical ﬁeld through role-playing and multi-turn interactions, generating more reliable responses.”
A Comprehensive Overview of Large Language Models (2023, unknown): “Less is more for alignment (LIMA) [166] uses only 1000 carefully created demonstrations to fine-tune the model and has achieved comparable performance to GPT-4.”
Empowering Biomedical Discovery with AI Agents (2024, unknown): “MedAgent [82] leverages the expertise of multiple medical AI agents for medical reasoning.”
Multi-Agent Collaboration Mechanisms: A Survey of LLMs (2025, unknown): “For instance, MedAgent [122] integrates medical agents with different specialties to deliver comprehensive analyses of patients’ conditions and treatment options.”
Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration (2026, unknown): “Such systems are typically tailored for specific tasks (like software development (Xiong et al. 2023), scientific experiments (Zheng et al. 2023), scientific debates (Tang et al. 2024, Du et al. 2023), social simulations (Park et al. 2022), etc.) and remain static once established.”