AI-powered in silico twins: redefining precision medicine through simulation, personalization, and predictive healthcare

In silico twins (ISTs) are emerging as a transformative paradigm in precision medicine, offering dynamic, high-fidelity representations of individual patients through real-time integration of multimodal data. In this work, we define an in silico twin (IST) as a high-fidelity, artificial intelligence(AI)-augmented computational replica of an individual's biological systems that integrates mechanistic modeling (e.g., PBPK, QSP) with patient-specific data streams to simulate, predict, and optimize therapeutic outcomes in real time. By combining AI, physiological and biomechanical modeling, and advanced simulation engines, these systems enable continuous monitoring, predictive diagnostics, and personalized treatment planning. Unlike conventional digital tools, ISTs provide iterative, adaptive simulations that evolve with patient states, fostering a shift from reactive to proactive healthcare. This review explores the technological foundations underpinning ISTs -including machine learning architectures, multi-scale physiological modeling, data integration, and cloud-edge infrastructure- and maps their clinical applications across the patient care continuum. We also distinguish ISTs from digital twins, virtual patients, and traditional computational models, emphasizing their unique contribution to decision support, drug development, and therapeutic optimization. As digital healthcare ecosystems mature, ISTs represent a crucial step toward simulation-driven, individualized medicine. Their continued development offers substantial potential for improving outcomes, accelerating discovery, and reshaping the clinical landscape. Uniquely, this review introduces a practical taxonomy of IST architectures, a verification and validation checklist for model credibility, and a deployment blueprint to guide their clinical translation and real-world adoption.