Medical digital twins: enabling precision medicine and medical artificial intelligence.

Journal: The Lancet. Digital health
Published Date: Jun 14, 2025

Abstract

The notion of medical digital twins is gaining popularity both within the scientific community and among the general public; however, much of the recent enthusiasm has occurred in the absence of a consensus on their fundamental make-up. Digital twins originate in the field of engineering, in which a constantly updating virtual copy enables analysis, simulation, and prediction of a real-world object or process. In this Health Policy paper, we evaluate this concept in the context of medicine and outline five key components of the medical digital twin: the patient, data connection, patient-in-silico, interface, and twin synchronisation. We consider how various enabling technologies in multimodal data, artificial intelligence, and mechanistic modelling will pave the way for clinical adoption and provide examples pertaining to oncology and diabetes. We highlight the role of data fusion and the potential of merging artificial intelligence and mechanistic modelling to address the limitations of either the AI or the mechanistic modelling approach used independently. In particular, we highlight how the digital twin concept can support the performance of large language models applied in medicine and its potential to address health-care challenges. We believe that this Health Policy paper will help to guide scientists, clinicians, and policy makers in creating medical digital twins in the future and translating this promising new paradigm from theory into clinical practice.

Authors

  • Christoph Sadee
    Stanford Center for Biomedical Informatics Research, Department of Medicine and Biomedical Data Science, Stanford University, California, USA.
  • Stefano Testa
    Division of Medical Oncology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Medicine, Stanford University, Stanford, CA, USA.
  • Thomas Barba
    Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford, CA, USA; Department of Internal Medicine, Édouard Herriot Hospital, Lyon, France.
  • Katherine Hartmann
    Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.
  • Maximilian Schuessler
    National Center for Tumor Diseases, Heidelberg University Hospital, Heidelberg, Germany.
  • Alexander Thieme
    Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford, CA, USA; Department of Radiation Oncology, Charité-Universitätsmedizin Berlin, Berlin, Germany.
  • George M Church
    Wyss Institute for Biologically Inspired Engineering , Boston, Massachusetts 02115, United States.
  • Ifeoma Okoye
    University of Nigeria Teaching Hospital, Ituku-Ozalla, Enugu, Nigeria; University of Nigeria, Nsukka Center of Excellence for Clinical Trials, Enugu, Nigeria.
  • Tina Hernandez-Boussard
    Stanford Center for Biomedical Informatics Research, Stanford, California 94305, USA.
  • Leroy Hood
    Institute for Systems Biology, Seattle, Washington.
  • Ilya Shmulevich
    Institute for Systems Biology, Seattle, WA 98109, USA.
  • Ellen Kuhl
    Department of Mechanical Engineering, Stanford University, Stanford, USA.
  • Olivier Gevaert
    Department of Biomedical Data Science, Stanford University, CA, 94305, USA.

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