Digital twin (DT) technology is revolutionizing clinical practice by integrating diverse epidemiological data sources to create dynamic, patient-specific simulations. By leveraging data from genomics, proteomics, imaging, sociodemographics, and real-world behaviors, DTs provide a computational framework to model disease progression, optimize treatments, and personalize health care interventions. Through artificial intelligence (AI) and mathematical modeling, DTs facilitate predictive analytics for disease risk assessment, early diagnosis, and treatment response forecasting. This viewpoint explores the mathematical foundations of DTs, including differential equations for health trajectory modeling, Bayesian networks for multiomics integration, Markov models for disease progression, and reinforcement learning for treatment optimization. In addition, machine learning techniques such as recurrent neural networks and transformers enhance the predictive power of DTs by analyzing time-series clinical data and predicting future health events. The potential applications of DTs extend beyond individual patient care to public health surveillance, hospital resource management, and epidemiological modeling. However, several challenges persist, including data privacy concerns, computational infrastructure requirements, validation of predictive models, and regulatory compliance. Addressing these limitations requires interdisciplinary collaboration among health care providers, data scientists, and policy makers. With advancements in AI, wearable technology, and multiomics data integration, DTs are poised to reshape precision medicine. Future research should focus on refining computational efficiency, standardizing data interoperability, and ensuring ethical AI-driven decision-making. The continued evolution of DTs offers a transformative approach to proactive and personalized health care, reducing disease burden and enhancing patient outcomes.
Alexandre Vallée (2025) studied this question.