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  • Review Article
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Application of large language models in medicine

Nature Reviews Bioengineering volume 3, pages 445–464 (2025) Cite this article

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Abstract

Large language models (LLMs), such as ChatGPT, have received great attention owing to their capabilities for understanding and generating human language. Despite a trend in researching the application of LLMs in supporting different medical tasks (such as enhancing clinical diagnostics and providing medical education), a comprehensive assessment of their development, practical applications and outcomes in the medical space is still missing. Therefore, this Review aims to provide an overview of the development and deployment of LLMs in medicine, including the challenges and opportunities they face. In terms of development, we discuss the principles of existing medical LLMs, including their basic model structures, number of parameters, and sources and scales of data used for model development. In terms of deployment, we compare different LLMs across various medical tasks and with state-of-the-art lightweight models.

Key points

  • Existing medical large language models (LLMs), ranging from 110 million to 520 billion parameters, are mainly developed through pre-training, fine-tuning and prompting methods using large-scale medical corpora from different sources.

  • Their performance is mostly evaluated based on exam-style question-answering tasks. Combining different fine-tuning and prompting methods enables LLMs to achieve comparable or even better results than experts.

  • LLMs perform poorly in non-question-answering tasks without pre-set options, thus requiring further improvements before integration into real clinical decision-making processes.

  • Medical LLMs are being adapted to various clinical applications, but large-scale clinical trials are still missing.

  • Mitigating hallucinations; establishing robust data, benchmarks and metrics; and addressing ethical, safety and regulatory concerns through interdisciplinary collaborations will help to accelerate the integration of LLMs into clinic practice.

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Fig. 1: Model size for different medical LLMs.
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Fig. 2: Performance comparison.
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Fig. 3: Development of medical LLMs over time.
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Fig. 4: Application of LLMs in medicine.
The alternative text for this image may have been generated using AI.

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Acknowledgements

This work was supported in part by the Pandemic Sciences Institute at the University of Oxford, the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, an NIHR Research Professorship, a Royal Academy of Engineering Research Chair, the Well-come Trust-funded VITAL project, the UK Research and Innovation, the Engineering and Physical Sciences Research Council, and the InnoHK Hong Kong Centre for Cerebro-cardiovascular Engineering (COCHE), the Clarendon Fund, and the Magdalen Graduate Scholarship.

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Author notes

  1. These authors contributed equally: Fenglin Liu, Hongjian Zhou, Boyang Gu, Xinyu Zou, Jinfa Huang, Jinge Wu.

  2. These authors jointly supervised this work: Fenglin Liu, Hongjian Zhou, Zheng Li, Jiebo Luo, David A. Clifton.

Authors and Affiliations

  1. Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK

    Fenglin Liu, Hongjian Zhou & David A. Clifton

  2. Department of Computing, Imperial College London, London, UK

    Boyang Gu

  3. Department of Systems Design Engineering, University of Waterloo, Waterloo, Ontario, Canada

    Xinyu Zou

  4. Department of Computer Science, University of Rochester, Rochester, NY, USA

    Jinfa Huang & Jiebo Luo

  5. Institute of Health Informatics, University College London, London, UK

    Jinge Wu

  6. Western University, London, Ontario, Canada

    Yiru Li

  7. Department of Kinesiology, University of Georgia, Athens, GA, USA

    Sam S. Chen

  8. Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Yining Hua

  9. Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

    Peilin Zhou

  10. School of ECE, Peking University, Shenzhen, China

    Junling Liu

  11. Massachusetts Institute of Technology, Cambridge, MA, USA

    Chengfeng Mao

  12. Stony Brook University, Stony Brook, NY, USA

    Chenyu You

  13. Jarvis Research Center, Tencent YouTu Laboratory, Beijing, China

    Xian Wu & Yefeng Zheng

  14. Medical Artificial Intelligence Laboratory, Westlake University, Hangzhou, China

    Yefeng Zheng

  15. Applied Digital Health (ADH), Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK

    Lei Clifton

  16. Amazon, Palo Alto, CA, USA

    Zheng Li

  17. Oxford-Suzhou Centre for Advanced Research, Suzhou, China

    David A. Clifton

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Contributions

H.Z. and F.L. conceived and designed the study. H.Z., F.L., B.G., X.Z., J.H. and J.W. conducted the literature review, performed data analysis and drafted the manuscript. All authors contributed to the interpretation and final manuscript preparation. All authors read and approved the final manuscript.

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Correspondence to Fenglin Liu.

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Related links

Alpaca: https://github.com/tatsu-lab/stanford_alpaca

Bard: https://gemini.google.com/

ChatGPT: https://chat.openai.com/

Claude-3: https://www.anthropic.com/news/claude-3-family

HealthcareMagic: https://www.healthcaremagic.com/

iCliniq: https://www.icliniq.com/

LLaMA-3: https://github.com/meta-llama/llama3

MedLlama3-v20: https://huggingface.co/ProbeMedicalYonseiMAILab/medllama3-v20

OpenBioLLM: https://huggingface.co/aaditya/OpenBioLLM-Llama3-70B

PubMed: https://pubmed.ncbi.nlm.nih.gov/

PubMed Central (PMC): https://www.ncbi.nlm.nih.gov/pmc/

ShareGPT: https://sharegpt.com/

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Liu, F., Zhou, H., Gu, B. et al. Application of large language models in medicine. Nat Rev Bioeng 3, 445–464 (2025). https://doi.org/10.1038/s44222-025-00279-5

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