Human digital twins in personalized and predictive healthcare: a comprehensive review of technologies, applications,...

The role of real-time data, artificial intelligence, and computational modeling is discussed in this review analytics Human Digital Twins (HDTs) creation- virtual persons of personalities patients which advocate predictive simulation to forecast of physiological behavior, treatment responses, and disease tracks. A synthesis of existing knowledge is done up to the technologies is a foundation to HDTs, clinical application and implementation issues of interest to precision medicine. The conceptual basis of engineering of the digital twins is analyzed and production principles, and technologies, which allow to produce HDTs-machine. Are physiological modeling, learning and distributed cloud-based computing infrastructure identified and evaluated. Cards: cardiology, oncology, genomics and immunology are critically appraised. It is based on the comparative analysis of 35 peer-reviewed documents and technical as it was reported, HDTs have great potential in enhancing personalized prediction of side effects, optimization of clinical trial design using virtual, and scheduling of treatment cohort simulation. But, model standards, an important component of model validation, are not present interoperability, ethical governing mechanisms and regulatory avenues to clinical deployment. The main priority research directions are determined, such as the development of common-validation techniques; implementation of federated learning frameworks to support sharing of data with data privacy limitations; incorporation of multi-omics data into physiological models; and introducing open ethical review procedures. This review provides substantive evidence basis to researchers, clinicians and policy makers to market the. Knowledge about HDTs technology to population health and health care provision revolutionizes.