Role of digital twins in longevity medicine

Digital twins represent virtual replicas enabling real-time monitoring, simulation, and prediction of complex systems. Originating from aerospace engineering in 2002, this paradigm has evolved into a transformative framework for healthcare. In medicine, digital twins are defined as viewable digital replicas of patients, organs, or physiological systems containing multidimensional, patient-specific information that informs clinical decision-making. These systems integrate data from wearable biosensors, multi-omics platforms, electronic health records, and medical imaging to create dynamic computational models powered by artificial intelligence and machine learning. Current applications span cardiovascular medicine (heart digital twins predicting arrhythmias and sudden cardiac death), orthopedics (decision support systems demonstrating improved outcomes in randomized trials), pain medicine (learning health systems transforming routine care), neurology (brain connectivity patterns identifying dementia risk), and oncology (patient-specific simulations optimizing ablation therapies). Challenges persist in data quality, model validation, workflow integration, algorithmic bias, privacy protection, regulatory frameworks, and reimbursement models. Digital twins hold transformative potential for longevity medicine by enabling the shift from reactive sick care to proactive healthcare through prospective, personalized, predictive, and preventive approaches that could extend healthspan through early disease interception and continuous health optimization.