Neuro-symbolic artificial intelligence in medicine

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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Funding

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

Author information

Authors and Affiliations

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

   Matthew Yu Heng Wong

2. Division of Pharmacy, Singapore General Hospital, Singapore, Singapore

   Jasmine Chiat Ling Ong

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

   Jasmine Chiat Ling Ong

4. Stanford University, Stanford, CA, USA

   Nigam Shah

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

   David C. Klonoff

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

   Bin Sheng

Authors

1. Matthew Yu Heng WongView author publications

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2. Jasmine Chiat Ling OngView author publications

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3. Nigam ShahView author publications

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4. David C. KlonoffView author publications

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5. Bin ShengView author publications

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Contributions

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

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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

• Published:10 July 2026

• Version of record:10 July 2026

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