Generative AI empowered digital twins for advancing precision medicine
Abstract Generative artificial intelligence (AI), enabled digital twins are emerging as a transformative paradigm for precision medicine, enabling dynamic, patient-specific modeling across clinical care, drug development, and population health. Digital twins provide computational representations of biological systems, while generative AI enhances their capacity for data synthesis, prediction, and simulation under uncertainty. Despite rapid technological progress, the clinical translation of these systems remains limited by challenges in validation, safety, interpretability, and regulatory oversight. In this work, we present a structured analysis of generative AI–powered digital twins in healthcare, spanning patient-level modeling, clinical trials, and preclinical drug discovery. Beyond reviewing existing applications, we introduce a clinical-grade evaluation framework encompassing validation strategies, uncertainty calibration, safety monitoring, and synthetic data governance. By articulating both opportunities and unresolved barriers, this study provides a roadmap for the responsible deployment of generative AI–enabled digital twins in precision medicine.
Authors
- Dedee Frances Murrell (ORCID: https://orcid.org/0000-0003-2971-0199)
- Jian Gong (ORCID: https://orcid.org/0000-0001-7214-1628)
- Alireza Haghighi
- Christopher G. Bunick (ORCID: https://orcid.org/0000-0002-4011-8308)
- Amirmohammad Pasdar (ORCID: https://orcid.org/0000-0001-5071-5803)
- Sancy Ann Leachman (ORCID: https://orcid.org/0000-0001-9140-0648)
- Mehdi Letafati (ORCID: https://orcid.org/0000-0003-3731-3943)
- Hossein Akbarialiabad (ORCID: https://orcid.org/0000-0003-2018-6378)
- Ayman Grada (ORCID: https://orcid.org/0000-0002-5321-0584)
- Cameron West (ORCID: https://orcid.org/0000-0001-9673-9165)
- Michelle Babb Tarbox (ORCID: https://orcid.org/0009-0003-3336-7394)
- Farhan Shakil
- Ehsan Safaee
- Maryam Janatifar
Institutions
- Broad Institute (US)
- Shahed University (IR)
- Texas Tech University (US)
- Brigham and Women's Hospital (US)
- University of Wyoming (US)
- BJC HealthCare (US)
- Harvard University (US)
- Oregon Health & Science University (US)
- The University of Melbourne (AU)
- Washington University in St. Louis (US)
- Sutherland Hospital (AU)
- Yale University (US)
- University School (US)
- Shahid Beheshti University (IR)
- Case Western Reserve University (US)
- Texas Tech University Health Sciences Center (US)
- Webster University (US)
- University of Oulu (FI)
Publication Details
- Journal
- Discover Artificial Intelligence
- Published
- 2026-03-27
- DOI
- https://doi.org/10.1007/s44163-026-01034-4
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00