Generative AI empowered digital twins for advancing precision medicine
Year: 2026
Type: article
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.
Source: Discover Artificial Intelligence
Institutions: Washington University in St. Louis, UNSW Sydney, Shahed University, University of Oulu, University of Wyoming, Oregon Health & Science University, Case Western Reserve University, The University of Melbourne, Shahid Beheshti University, Webster University, Yale University, Texas Tech University Health Sciences Center, Broad Institute, Brigham and Women's Hospital, Harvard University
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,590
OpenAlex ID: W7141281381