Digital Twins in Personalized Medicine: Bridging Innovation and Clinical Reality

Digital Twins (DTs) are poised to transform personalized medicine by enabling real-time, multiscale simulations of individual patients. By integrating genomics, imaging, wearable sensor data, and clinical records, DTs offer a powerful platform for predictive, adaptive, and patient-centered decision-making. Recent advances have highlighted their potential across a range of clinical domains, including cardiology, oncology, pharmacogenomics, and neurology. Yet, their routine application in clinical practice remains limited, underscoring a growing translational gap between digital innovation and healthcare delivery. In this review, we explore the scientific maturity and emerging clinical use cases of DTs, while critically analyzing the systemic, regulatory, ethical, and infrastructural barriers that hinder their widespread adoption. We outline a translational roadmap that emphasizes dynamic model validation, clinician co-development, equitable data representation, and regulatory harmonization. Uniquely, we reframe DTs as cognitive tools for clinical reasoning and decision support. We further clarify translational pathways through explicit evaluation and reporting recommendations. By positioning DTs within this practical framework, we outline how responsible, inclusive, and interdisciplinary implementation can establish them as foundational elements of 21st century precision medicine.