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Scaling medical AI across clinical contexts

Nature Medicine (2026)Cite this article

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Abstract

Medical artificial intelligence (AI) tools, including clinical language models, vision–language models and multimodal health record models, are used to summarize clinical notes, answer questions and support decisions. Their adaptation to new populations, specialties or care settings often relies on fine-tuning, prompting or retrieval from external knowledge bases. These strategies can scale poorly and risk contextual errors—outputs that appear plausible but miss critical patient or situational information. We envision context switching as an emergent solution. Context switching adjusts model reasoning at inference, without retraining. Generative models can tailor outputs to patient biology, care setting or disease. Multimodal models can switch between notes, laboratory results, imaging and genomics, even when some data are missing or delayed. Agent models can coordinate tools and roles based on task and user context. In each case, context switching enables medical AI to adapt across specialties, populations and geographies. This approach requires advances in data design, model architectures and evaluation frameworks, and establishes a foundation for medical AI that scales to an infinite number of contexts, while remaining reliable and suited to real-world care.

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Fig. 1: From pretraining to contextual adaptation in medical AI.
Fig. 2: Context switching across medical specialties and diseases, geographies and populations, and healthcare roles.

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Acknowledgements

M.M.L. is supported by The Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute. We gratefully acknowledge the support of NIH R01-HD108794, NSF CAREER 2339524, US DoD FA8702-15-D-0001, ARPA-H BDF program, Chan Zuckerberg Initiative, Bill & Melinda Gates Foundation INV-079038, Amazon Faculty Research, Google Research Scholar Program, AstraZeneca Research, Roche Alliance with Distinguished Scientists, Sanofi iDEA-iTECH, Pfizer Research, GlaxoSmithKline Award, Boehringer Ingelheim Award, Merck Award, Optum AI Research Collaboration Award, John and Virginia Kaneb Fellowship at Harvard Medical School, Biswas Computational Biology Initiative in partnership with the Milken Institute, Harvard Medical School Dean’s Innovation Fund for the Use of Artificial Intelligence, and Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funders.

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Authors and Affiliations

  1. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Michelle M. Li, Ben Y. Reis, Tianxi Cai, Isaac S. Kohane & Marinka Zitnik

  2. The Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute, Boston, MA, USA

    Michelle M. Li, Ben Y. Reis, Noa Dagan, Ran D. Balicer, Isaac S. Kohane & Marinka Zitnik

  3. Predictive Medicine Group, Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA, USA

    Ben Y. Reis

  4. Harvard Data Science Initiative, Cambridge, MA, USA

    Ben Y. Reis & Marinka Zitnik

  5. Division of General Internal Medicine, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA

    Adam Rodman

  6. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Tianxi Cai

  7. Clalit Research Institute, Innovation Division, Clalit Health Services, Ramat-Gan, Israel

    Noa Dagan & Ran D. Balicer

  8. Software and Information Systems Engineering, Ben Gurion University, Be’er Sheva, Israel

    Noa Dagan

  9. Faculty of Health Sciences, School of Public Health, Ben Gurion University of the Negev, Be’er Sheva, Israel

    Ran D. Balicer

  10. Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Joseph Loscalzo

  11. Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University, Allston, MA, USA

    Marinka Zitnik

  12. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Marinka Zitnik

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Contributions

All authors contributed to the writing of the paper and provided critical feedback. M.M.L. and M.Z. conceived the study and were responsible for overall direction and planning.

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Correspondence to Michelle M. Li or Marinka Zitnik.

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Li, M.M., Reis, B.Y., Rodman, A. et al. Scaling medical AI across clinical contexts. Nat Med (2026). https://doi.org/10.1038/s41591-025-04184-7

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