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.