Small language models can serve as a more scalable, practical and efficient alternative for medical applications compared to large language models. Here we discuss the strengths and limitations, challenges and future directions of small language models in healthcare implementation, highlighting their potential to enable broader real-world adoption across diverse clinical settings.
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Funding
This work was supported by the National Key R&D Program of China (grant no. 2022YFC2502800), the National Natural Science Foundation of China (grant no. 82388101) and the Beijing Natural Science Foundation (grant no. IS23096).
Authors and Affiliations
Contributions
T.Y.W. conceived and supervised the project. Y.Q., T.Y. and M.Y.H.W. wrote the paper. S.S., H.-Y.Z., R.E., Y.-C.T., N.H.S., J.W., H.W. and T.Y.W. reviewed and edited the paper. All authors provided critical comments and reviewed the paper. All authors discussed the results and approved the final version before submission.
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The authors declare no competing interests.
Peer review
Peer review information
Nature Biomedical Engineering thanks Hyunjae Kim and Jasmine Ong for their contribution to the peer review of this work.
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Qin, Y., Yan, T., Wong, M.Y.H. et al. Small language models in medicine.
Nat. Biomed. Eng (2026). https://doi.org/10.1038/s41551-026-01734-3
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Version of record:13 July 2026
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DOI
:https://doi.org/10.1038/s41551-026-01734-3
