Leveraging Digital Twin Technology for Enhanced Patient Care and Predictive Health Management

Digital twin (DT) technology is emerging as a disruptive paradigm in healthcare, offering unprecedented opportunities for data-driven, predictive, and patient-centric solutions. Unlike prior descriptive reviews, this paper contributes a conceptual framework for patient-specific digital twins that integrates enabling technologies, clinical applications, and ethical–regulatory considerations. The framework illustrates how DTs combine real-time physiological data, artificial intelligence (AI), simulation models, and metaverse-based visualization to create dynamic representations of patient health. Key applications are highlighted in disease monitoring, surgical planning, personalized drug development, and hospital management. Beyond applications, the paper provides a critical examination of data ownership, algorithmic bias, patient trust, and governance mechanisms, areas often underexplored in existing literature. To strengthen academic rigor, we adopt a narrative review methodology drawing from recent studies (2019–2024) across PubMed, Scopus, IEEE Xplore, and Web of Science. The findings suggest that while DTs hold transformative potential, unresolved challenges in data integration, validation, and equitable access remain barriers to widespread adoption. The paper concludes with future research directions and policy recommendations, positioning DTs as a cornerstone for next-generation healthcare systems.