---
title: "MedAgents: Large Language Models as — Tang 2024"
description: "LLMs handle many general tasks well, but medical questions remain hard."
authors: ["Xiangru Tang", "Anni Zou", "Zhuosheng Zhang", "Ziming Li", "Yilun Zhao", "Xingyao Zhang", "Arman Cohan", "Mark Gerstein"]
year: 2024
venue: "Findings of the Association for Computational Linguistics: ACL 2024"
source: "https://aclanthology.org/2024.findings-acl.33/"
doi: "10.18653/v1/2024.findings-acl.33"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/tang-2024-medagents-large-language-models-as/"
---
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# MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning

**Authors:** [Xiangru Tang](https://md.research.elamur.ai/free/en/authors/xiangru-tang/), Anni Zou, [Zhuosheng Zhang](https://md.research.elamur.ai/free/en/authors/zhuosheng-zhang/), Ziming Li, [Yilun Zhao](https://md.research.elamur.ai/free/en/authors/yilun-zhao/), Xingyao Zhang, [Arman Cohan](https://md.research.elamur.ai/free/en/authors/arman-cohan/), [Mark Gerstein](https://md.research.elamur.ai/free/en/authors/mark-gerstein/) · **Institutions:** [Shanghai Jiao Tong University](https://md.research.elamur.ai/free/en/universities/shanghai-jiao-tong-university/) · [Yale University](https://md.research.elamur.ai/free/en/universities/yale-university/) · **Year:** 2024 · **Venue:** Findings of the Association for Computational Linguistics: ACL 2024 · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2024.findings-acl.33/) · [PDF](https://aclanthology.org/2024.findings-acl.33.pdf) · [DOI 10.18653/v1/2024.findings-acl.33](https://doi.org/10.18653/v1/2024.findings-acl.33) · [OpenAlex](https://openalex.org/W4402670791)

**Citations:** 149 citations (OpenAlex, 2026-10-14) · [95](https://md.research.elamur.ai/free/en/papers/tang-2024-medagents-large-language-models-as/cited-by/) in our library, of the 140884 papers whose bibliographies we read

## Summary
LLMs handle many general tasks well, but medical questions remain hard. The field relies on specialized terminology, public clinical training data is scarce, and plain prompting often fails to bring out expert knowledge or invites hallucination. MedAgents addresses this with a training-free, zero-shot framework that mimics a hospital multi-disciplinary consultation. Several LLM agents act as specialists chosen for the question and its answer options. Each writes an analysis. These are combined into a draft report, which the agents revise over repeated rounds until all agree, and a final answer is taken from the agreed report. The authors evaluate it on nine benchmarks: MedQA, MedMCQA, PubMedQA and six medical subtasks of MMLU. The zero-shot method beats zero-shot chain-of-thought and self-consistency baselines and is reported to match or exceed 5-shot baselines. Human error analysis shows where the method still fails. The work matters because it offers a plug-and-play way to draw out latent medical knowledge without fine-tuning or retrieval augmentation (RAG).

## Key points
- The pipeline has five stages: expert gathering, analysis proposition, report summarization, collaborative consultation, and decision making.
- Separate expert groups are recruited for the question and for the answer options, so the framework draws on more varied knowledge.
- The evaluation follows the Flan-PaLM setting. It covers nine datasets, including MedQA (USMLE), MedMCQA (6.1K test items from AIIMS/NEET PG exams), PubMedQA (yes/no/maybe questions over PubMed abstracts) and six medical subsets of MMLU.
- Human evaluation found four error types: missing domain knowledge, wrongly retrieved knowledge, consistency errors, and CoT errors.
- The authors list three limitations: the models' built-in knowledge goes out of date, mixing different LLMs across stages is untested, and transfer to low-resource languages is limited. Code is available at github.com/gersteinlab/MedAgents.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

Topics: [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{tang2024medagents,
  title = {{MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning}},
  author = {Tang, Xiangru and Zou, Anni and Zhang, Zhuosheng and Li, Ziming and Zhao, Yilun and Zhang, Xingyao and Cohan, Arman and Gerstein, Mark},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2024},
  year = {2024},
  doi = {10.18653/v1/2024.findings-acl.33},
  url = {https://doi.org/10.18653/v1/2024.findings-acl.33}
}
```

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64. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. 2022. React: Synergizing reasoning and acting in language models. arXiv preprint arXiv:2210.03629. [find](https://scholar.google.com/scholar?q=Shunyu+Yao%2C+Jeffrey+Zhao%2C+Dian+Yu%2C+Nan+Du%2C+Izhak+Shafran%2C+Karthik+Narasimhan%2C+and+Yuan+Cao%2E+2022%2E+React%3A+Synergizing+reasoning+and+acting+in+language+models%2E+arXiv+preprint+arXiv%3A2210%2E03629%2E) [our card](https://md.research.elamur.ai/free/en/papers/yao-2022-react-synergizing-reasoning-acting-language/)
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66. Zekun Moore Wang, Zhongyuan Peng, Haoran Que, Jiaheng Liu, Wangchunshu Zhou, Yuhan Wu, Hongcheng Guo, Ruitong Gan, Zehao Ni, Man Zhang, Zhaoxiang Zhang, Wanli Ouyang, Ke Xu, Wenhu Chen, Jie Fu, and Junran Peng. 2023c. Rolellm: Benchmarking, eliciting, and enhancing role-playing abilities of large la… [find](https://scholar.google.com/scholar?q=Zekun+Moore+Wang%2C+Zhongyuan+Peng%2C+Haoran+Que%2C+Jiaheng+Liu%2C+Wangchunshu+Zhou%2C+Yuhan+Wu%2C+Hongcheng+Guo%2C+Ruitong+Gan%2C+Zehao+Ni%2C+Man+Zhang%2C+Zhaoxiang+Zhang%2C+Wanli+Ouyang%2C+Ke+Xu%2C+Wenhu+Chen%2C+Jie+Fu%2C+and+Ju) [our card](https://md.research.elamur.ai/free/en/papers/wang-2023-rolellm-benchmarking-eliciting-enhancing-role/)
67. Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, and Heng Ji. 2023d. Unleashing cognitive synergy in large language models: A task-solving agent through multi-persona self-collaboration. [find](https://scholar.google.com/scholar?q=Zhenhailong+Wang%2C+Shaoguang+Mao%2C+Wenshan+Wu%2C+Tao+Ge%2C+Furu+Wei%2C+and+Heng+Ji%2E+2023d%2E+Unleashing+cognitive+synergy+in+large+language+models%3A+A+task%2Dsolving+agent+through+multi%2Dpersona+self%2Dcollaboration%2E)
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69. Cyril Zakka, Akash Chaurasia, Rohan Shad, Alex R Dalal, Jennifer L Kim, Michael Moor, Kevin Alexander, Euan Ashley, Jack Boyd, Kathleen Boyd, et al. 2023. Almanac: Retrieval-augmented language models for clinical medicine. Research Square. [find](https://scholar.google.com/scholar?q=Cyril+Zakka%2C+Akash+Chaurasia%2C+Rohan+Shad%2C+Alex+R+Dalal%2C+Jennifer+L+Kim%2C+Michael+Moor%2C+Kevin+Alexander%2C+Euan+Ashley%2C+Jack+Boyd%2C+Kathleen+Boyd%2C+et+al%2E+2023%2E+Almanac%3A+Retrieval%2Daugmented+language+models+)
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73. Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, and Chi Wang. 2023a. Autogen: Enabling next-gen llm applications via multi-agent conversation framework. arXiv preprint arXiv:2308.08155. [find](https://scholar.google.com/scholar?q=Qingyun+Wu%2C+Gagan+Bansal%2C+Jieyu+Zhang%2C+Yiran+Wu%2C+Shaokun+Zhang%2C+Erkang+Zhu%2C+Beibin+Li%2C+Li+Jiang%2C+Xiaoyun+Zhang%2C+and+Chi+Wang%2E+2023a%2E+Autogen%3A+Enabling+next%2Dgen+llm+applications+via+multi%2Dagent+convers) [our card](https://md.research.elamur.ai/free/en/papers/wu-2023-autogen-enabling-next-gen-llm/)
74. Yiquan Wu, Siying Zhou, Yifei Liu, Weiming Lu, Xiaozhong Liu, Yating Zhang, Changlong Sun, Fei Wu, and Kun Kuang. 2023b. Precedent-enhanced legal judgment prediction with llm and domain-model collaboration. [find](https://scholar.google.com/scholar?q=Yiquan+Wu%2C+Siying+Zhou%2C+Yifei+Liu%2C+Weiming+Lu%2C+Xiaozhong+Liu%2C+Yating+Zhang%2C+Changlong+Sun%2C+Fei+Wu%2C+and+Kun+Kuang%2E+2023b%2E+Precedent%2Denhanced+legal+judgment+prediction+with+llm+and+domain%2Dmodel+collabor)
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76. Tianbao Xie, Fan Zhou, Zhoujun Cheng, Peng Shi, Luoxuan Weng, Yitao Liu, Toh Jing Hua, Junning Zhao, Qian Liu, Che Liu, et al. 2023. Openagents: [find](https://scholar.google.com/scholar?q=Tianbao+Xie%2C+Fan+Zhou%2C+Zhoujun+Cheng%2C+Peng+Shi%2C+Luoxuan+Weng%2C+Yitao+Liu%2C+Toh+Jing+Hua%2C+Junning+Zhao%2C+Qian+Liu%2C+Che+Liu%2C+et+al%2E+2023%2E+Openagents%3A)