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Large language models in medicine

Nature Medicine volume 29, pages 1930–1940 (2023)Cite this article

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Abstract

Large language models (LLMs) can respond to free-text queries without being specifically trained in the task in question, causing excitement and concern about their use in healthcare settings. ChatGPT is a generative artificial intelligence (AI) chatbot produced through sophisticated fine-tuning of an LLM, and other tools are emerging through similar developmental processes. Here we outline how LLM applications such as ChatGPT are developed, and we discuss how they are being leveraged in clinical settings. We consider the strengths and limitations of LLMs and their potential to improve the efficiency and effectiveness of clinical, educational and research work in medicine. LLM chatbots have already been deployed in a range of biomedical contexts, with impressive but mixed results. This review acts as a primer for interested clinicians, who will determine if and how LLM technology is used in healthcare for the benefit of patients and practitioners.

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Fig. 1: LLMs developed in recent years.
Fig. 2: Fine-tuning an LLM (GPT-3.5) to develop an LLM chatbot (ChatGPT).
Fig. 3: Limitations, priorities for research and development and potential use-cases of LLM applications.

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Acknowledgements

D.S.W.T. is supported by the National Medical Research Council, Singapore (NMCR/HSRG/0087/2018, MOH-000655-00 and MOH-001014-00), the Duke-NUS Medical School (Duke-NUS/RSF/2021/0018 and 05/FY2020/EX/15-A58) and the Agency for Science, Technology and Research (A20H4g2141 and H20C6a0032). These funders were not involved in the conception, execution or reporting of this review.

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Authors and Affiliations

  1. University of Cambridge School of Clinical Medicine, Cambridge, UK

    Arun James Thirunavukarasu

  2. Corpus Christi College, University of Cambridge, Cambridge, UK

    Arun James Thirunavukarasu

  3. Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK

    Darren Shu Jeng Ting

  4. Birmingham and Midland Eye Centre, Birmingham, UK

    Darren Shu Jeng Ting

  5. Academic Ophthalmology, School of Medicine, University of Nottingham, Nottingham, UK

    Darren Shu Jeng Ting

  6. Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore

    Kabilan Elangovan, Laura Gutierrez, Ting Fang Tan & Daniel Shu Wei Ting

  7. Department of Ophthalmology and Visual Sciences, Duke-National University of Singapore Medical School, Singapore, Singapore

    Ting Fang Tan & Daniel Shu Wei Ting

  8. Byers Eye Institute, Stanford University, Palo Alto, CA, USA

    Daniel Shu Wei Ting

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D.S.W.T. holds a patent on a deep learning system for the detection of retinal diseases. The other authors declare no conflicts of interest.

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Thirunavukarasu, A.J., Ting, D.S.J., Elangovan, K. et al. Large language models in medicine. Nat Med 29, 1930–1940 (2023). https://doi.org/10.1038/s41591-023-02448-8

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  • DOI: https://doi.org/10.1038/s41591-023-02448-8

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