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Generative artificial intelligence in medicine

Nature Medicine (2025)Cite this article

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Abstract

Generative artificial intelligence (GAI) can automate a growing number of biomedical tasks, ranging from clinical decision support to design and analysis of research studies. GAI uses machine learning and transformer model architectures to generate useful text, images and sound data in response to user queries. While previous biomedical deep-learning applications have used general-purpose datasets and enormous volumes of labeled data for training, evidence now suggests that GAI models may perform better while requiring less training data—for example, using smaller, domain-specific datasets. Moreover, AI techniques have progressed from fully supervised training to less label-intensive approaches, such as weakly supervised or unsupervised fine-tuning and reinforcement learning. Recent iterations of GAI, such as agents, mixture-of-expert models and reasoning models, have further extended their capabilities to assist with complex and multistage tasks. Here, we provide an overview of recent technical advancements in GAI. We explore the potential of the latest generation of models to improve healthcare for clinicians and patients, and discuss validation approaches using specific examples to illustrate challenges and opportunities for further work.

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Fig. 1: Overview of the GAI development pipeline.
Fig. 2: GAI development pipeline based on specific modalities.

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Acknowledgements

National Medical Research Council Singapore (MOH-000655-00/MOH-001014-00), Duke-NUS Medical School (Duke-NUS/RSF/2021/001805/FY2020/EX/15-A5805/FY2022/EX/66-Q128) and Agency for Science, Technology and Research (H20C6a0032).

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Author notes

  1. These authors contributed equally: Zhen Ling Teo, Arun James Thirunavukarasu.

Authors and Affiliations

  1. Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore

    Zhen Ling Teo, Kabilan Elangovan, Haoran Cheng, Prasanth Moova, Andreas Pollreisz, Darren Shu Jeng Ting & Daniel Shu Wei Ting

  2. AI Office, Singapore Health Services, Singapore, Singapore

    Zhen Ling Teo, Kabilan Elangovan, Prasanth Moova & Daniel Shu Wei Ting

  3. Nuffield Department of Clinical Neurosciences, Medical Sciences Division, University of Oxford, Oxford, UK

    Arun James Thirunavukarasu

  4. Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore, Singapore

    Haoran Cheng & Darren Shu Jeng Ting

  5. Department of Ophthalmology, Byers Eye Institute, Stanford, CA, USA

    Brian Soetikno & Daniel Shu Wei Ting

  6. Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada

    Christopher Nielsen

  7. Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria

    Andreas Pollreisz

  8. Department of Inflammation and Ageing, College of Medicine and Health, University of Birmingham, Birmingham, UK

    Darren Shu Jeng Ting

  9. Birmingham and Midland Eye Centre, Sandwell and West Birmingham NHS Trust, Birmingham, UK

    Darren Shu Jeng Ting

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

    Darren Shu Jeng Ting

  11. Ministry of Health (MOH) Office for Healthcare Transformation, Singapore, Singapore

    Robert J. T. Morris

  12. Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Robert J. T. Morris

  13. Department of Medicine, Stanford University, Stanford, CA, USA

    Nigam H. Shah

  14. Department of Radiology, Stanford University, Stanford, CA, USA

    Curtis P. Langlotz

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Teo, Z.L., Thirunavukarasu, A.J., Elangovan, K. et al. Generative artificial intelligence in medicine. Nat Med (2025). https://doi.org/10.1038/s41591-025-03983-2

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