---
title: "A Survey of Large Language Models in Medicine: Progress,"
description: "This survey maps how large language models are being adapted to medicine. 271 references, cited by 47 papers here."
authors: ["Hongjian Zhou", "Fenglin Liu", "Boyang Gu", "Xinyu Zou", "Jinfa Huang", "Jinge Wu", "Yiru Li", "Sam S. Chen", "Peilin Zhou", "Junling Liu", "Yining Hua", "Chengfeng Mao", "Chenyu You", "Xian Wu", "Yefeng Zheng", "Lei Clifton", "Zheng Li", "Jiebo Luo", "David A. Clifton"]
year: 2023
venue: "arXiv:2311.05112"
source: "https://arxiv.org/abs/2311.05112"
doi: "10.48550/arxiv.2311.05112"
arxiv: "2311.05112"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhou-2023-survey-large-language-models-medicine/"
---
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# A Survey of Large Language Models in Medicine: Progress, Application, and Challenge

**Authors:** Hongjian Zhou, Fenglin Liu, Boyang Gu, Xinyu Zou, Jinfa Huang, Jinge Wu, Yiru Li, Sam S. Chen, Peilin Zhou, Junling Liu, Yining Hua, [Chengfeng Mao](https://md.research.elamur.ai/free/en/authors/chengfeng-mao/), Chenyu You, Xian Wu, Yefeng Zheng, Lei Clifton, [Zheng Li](https://md.research.elamur.ai/free/en/authors/zheng-li/), Jiebo Luo and David A. Clifton · **Year:** 2023 · **Venue:** arXiv:2311.05112 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2311.05112) · [PDF](https://arxiv.org/pdf/2311.05112) · [DOI 10.48550/arxiv.2311.05112](https://doi.org/10.48550/arxiv.2311.05112) · [arXiv 2311.05112](https://arxiv.org/abs/2311.05112) · [OpenAlex](https://openalex.org/W4388584897)

**Citations:** 40 citations (OpenAlex, 2026-10-14) · [47](https://md.research.elamur.ai/free/en/papers/zhou-2023-survey-large-language-models-medicine/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
This survey maps how large language models are being adapted to medicine. It covers how they are built, how well they perform, and what blocks their clinical use. Earlier reviews mostly described ChatGPT-style applications through small case studies. They offered little guidance on constructing medical models or evaluating them systematically.

The authors organize the field around five questions:
- development practices
- evaluation
- real-world deployment
- challenges
- future directions

They catalogue existing medical LLMs by architecture, parameter count, and training data sources. They group construction strategies into three routes: pre-training from scratch, fine-tuning general models, and prompting. They compare LLMs with lightweight state-of-the-art models across ten biomedical NLP tasks and outline seven clinical application scenarios.

The central finding is that LLMs now rival experts on exam-style multiple-choice QA. However, they remain weak on open-ended tasks that resemble real clinical work. The review is useful as a practical map for teams designing domain LLMs under constraints on compute, privacy, or local knowledge.

## Key points
- Surveyed medical LLMs range from 110 million to 520 billion parameters. Examples include ChatDoctor, MedAlpaca, PMC-LLaMA, BenTsao, and Clinical Camel, many of them built on LLaMA.
- On USMLE, MedPaLM-2 (based on PaLM) scores 86.5 and MedPrompt (based on GPT-4) scores 90.2, against an expert reference of 87.0.
- Results on non-QA tasks without preset answer options are poor. The authors also note that large-scale clinical trials of medical LLMs are still missing.
- The main open problems are:
  - hallucination
  - scarce domain data
  - inadequate benchmarks and metrics
  - behavior alignment
  - ethical, safety, and regulatory issues
- Proposed future directions include multimodal LLMs (including time-series signals such as ECG), collaborative multi-agent "specialist" systems, underrepresented specialties like sports medicine, and interdisciplinary "doctor-in-the-loop" development. A living resource list is maintained at github.com/AI-in-Health/MedLLMsPracticalGuide.

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

## Institutions

- [Massachusetts Institute of Technology](https://md.research.elamur.ai/free/en/universities/massachusetts-institute-of-technology/) — University, Cambridge, United States · 159 works · 65,157 citations
- [University of Oxford](https://md.research.elamur.ai/free/en/universities/university-of-oxford/) — University, Oxford, United Kingdom · 52 works · 154,829 citations
- [Harvard University](https://md.research.elamur.ai/free/en/universities/harvard-university/) — University, Cambridge, United States · 73 works · 37,309 citations
- [Tencent](https://md.research.elamur.ai/free/en/companies/tencent/) — Company, Shenzhen, China · 66 works · 17,945 citations
- [Alibaba Group](https://md.research.elamur.ai/free/en/companies/alibaba-group/) — Company, Hangzhou, China · 64 works · 14,113 citations
- [University College London](https://md.research.elamur.ai/free/en/universities/university-college-london/) — University, London, United Kingdom · 59 works · 16,463 citations
- [Amazon](https://md.research.elamur.ai/free/en/companies/amazon/) — Company, Seattle, United States · 70 works · 39,977 citations
- [Yale University](https://md.research.elamur.ai/free/en/universities/yale-university/) — University, New Haven, United States · 54 works · 11,964 citations
- [University of Waterloo](https://md.research.elamur.ai/free/en/universities/university-of-waterloo/) — University, Waterloo, Canada · 44 works · 12,783 citations
- [University of Rochester](https://md.research.elamur.ai/free/en/universities/university-of-rochester/) — University, Rochester, United States · 25 works · 6,799 citations
- [Imperial College London](https://md.research.elamur.ai/free/en/universities/imperial-college-london/) — University, London, United Kingdom · 24 works · 8,272 citations
- [The Hong Kong University of Science and Technology (Guangzhou)](https://md.research.elamur.ai/free/en/universities/the-hong-kong-university-of-science-and-technology-guangzhou/) — University, Guangzhou, China · 7 works · 1,026 citations
- [University of Georgia](https://md.research.elamur.ai/free/en/universities/university-of-georgia/) — University, Athens, United States · 9 works · 26,161 citations
- [Western University](https://md.research.elamur.ai/free/en/universities/western-university/) — University, London, Canada · 4 works · 204 citations

📚 Shelves (5): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 9,086 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 8,889 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 1,282 works · [🩺 Medicine](https://md.research.elamur.ai/free/en/shelves/medicine/) · 80 works · [🩺 Health Informatics](https://md.research.elamur.ai/free/en/shelves/medicine/health-informatics/) · 53 works

🧭 Topics (1): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 1,343 works

## Cite

Cite as: Hongjian Zhou, Fenglin Liu, Boyang Gu, Xinyu Zou, Jinfa Huang, Jinge Wu, Yiru Li, Sam S. Chen, Peilin Zhou, Junling Liu, Yining Hua, Chengfeng Mao, Chenyu You, Xian Wu, Yefeng Zheng, Lei Clifton, Zheng Li, Jiebo Luo and David A. Clifton. 2023. A Survey of Large Language Models in Medicine: Progress, Application, and Challenge. arXiv:2311.05112.

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

```bibtex
@misc{zhou2023survey,
  title = {{A Survey of Large Language Models in Medicine: Progress, Application, and Challenge}},
  author = {Hongjian Zhou, Fenglin Liu, Boyang Gu, Xinyu Zou, Jinfa Huang, Jinge Wu, Yiru Li, Sam S. Chen, Peilin Zhou, Junling Liu, Yining Hua, Chengfeng Mao, Chenyu You, Xian Wu, Yefeng Zheng, Lei Clifton, Zheng Li, Jiebo Luo and David A. Clifton},
  year = {2023},
  doi = {10.48550/arxiv.2311.05112},
  eprint = {2311.05112},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2311.05112}
}
```

- [← previous On the Tool Manipulation Capability of Open-source Large Language Models](https://md.research.elamur.ai/free/en/papers/xu-2023-tool-manipulation-capability-open-source/)
- [next → Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey](https://md.research.elamur.ai/free/en/papers/kumar-2023-language-generation-models-can-cause/)
- [↑ back to papers of 2023](https://md.research.elamur.ai/free/en/papers/2023/page/6/)

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*Updated 24 October 2026*