Neuro

Neuro-symbolic approaches may address long-standing limitations in biomedical artificial intelligence by integrating data-driven inference with explicit clinical knowledge to improve safety, transparency and accountability.

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This work was supported by the National Natural Science Foundation of China (grant no. T2525004).

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

Division of Pharmacy, Singapore General Hospital, Singapore, Singapore

Duke-NUS AI + Medical Sciences Initiative, Duke-NUS Medical School, Singapore, Singapore

Stanford University, Stanford, CA, USA

Diabetes Research Institute, Mills-Peninsula Medical Center (Sutter Health), San Mateo, CA, USA

School of Computer Science, Shanghai Jiao Tong University, Shanghai, China

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M.Y.H.W. and B.S. conceived the project. M.Y.H.W. wrote the paper. J.C.L.O., D.C.K., N.S. and B.S. reviewed and edited the paper. B.S supervised the project. All authors discussed and approved the final version before submission.

The authors declare no competing interests.

Wong, M.Y.H., Ong, J.C.L., Shah, N. et al. Neuro-symbolic artificial intelligence in medicine. Nat. Biomed. Eng (2026). https://doi.org/10.1038/s41551-026-01728-1

Version of record: 10 July 2026

DOI: https://doi.org/10.1038/s41551-026-01728-1