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Towards credible digital twins for basic and preclinical research

Nature Reviews Methods Primers volume 6, Article number: 5 (2026) Cite this article

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Digital twins are well established in industrial settings, but there has not been wide adoption in biomedical settings. Digital twins for biomedical applications are now possible with the inclusion of artificial intelligence and the potential to combine mechanistic and clinical models that learn and adjust for human variability.

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Author information

Authors and Affiliations

  1. Center for Precision Medicine and Data Sciences, University of California, Davis, Davis, CA, USA

    Colleen E. Clancy

  2. Department of Physiology and Membrane Biology, University of California, Davis, Davis, CA, USA

    Colleen E. Clancy

  3. Department of Pharmacology, University of California, Davis, Davis, CA, USA

    Colleen E. Clancy

  4. Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA

    Bennett A. Landman

Authors
  1. Colleen E. Clancy
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  2. Bennett A. Landman
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Correspondence to Colleen E. Clancy.

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The authors declare no competing interests.

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Clancy, C.E., Landman, B.A. Towards credible digital twins for basic and preclinical research. Nat Rev Methods Primers 6, 5 (2026). https://doi.org/10.1038/s43586-025-00454-3

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