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Digital Twin technology revolutionizes modern medicine with the creation of real-time virtual models of patients, organs, and healthcare systems; all of these improve diagnosis, treatment, and management. From industrial applications, DTs moved to the healthcare sector driven by advancements in AI, IoMT, big data analytics, and cloud computing. This review synthesizes recent findings from systematic literature reviews across databases such as PubMed, Scopus, and Web of Science, focusing on key themes including personalized medicine, predictive modeling, virtual clinical trials, and optimization of healthcare systems. Such applications are performed with the intention of minimizing risks within treatments and enhancing patients' experiences. In contrast, substantial obstacles to this goal include data privacy, systemic incompatibility, cyber threats, and regulatory problems that all act to limit the wide diffusion of these technologies. Overcoming these challenges will require ethical governance, standardized systems, and interdisciplinary collaboration for full empowerment of DTs. Ongoing technological advancements strive to create a healthcare environment that is efficient, predictive, and focused on patients' experiences, exemplified by the capability to detect early warnings from subtle indicators in physiological monitoring.
Menon et al. (Thu,) studied this question.