Healthcare digital twins: A methodological literature review on integrating iot and AI for personalized medicine and predictive care

Digital Twin (DT) technology has the potential to revolutionize healthcare delivery and enhance patient outcomes through personalized and precision medicine, simulation models for operations and interventions, and drug discovery. However, successful implementation of DTs in Internet of Things (IoT) and artificial intelligence (AI) healthcare is contingent upon addressing key challenges such as privacy, ethics, and robust data security. This paper presents a methodological literature review of DT applications in healthcare, systematically analyzing the current state of research, key enabling technologies, and implementation challenges. The review summarizes DT categorization approaches (application-based, technology-based, and real-time function-based); delineates core DT components such as sensors, data pipelines, AI/ML capabilities, security and governance measures; and surveys data collection and sensing technologies spanning EHRs, wearable/IoMT devices, and medical imaging. It further synthesizes diverse case studies across hospital management, diagnosis and treatment, patient monitoring and management, personalized therapies, and medical devices, highlighting both performance gains and translational gaps. Based on the corpus, the review identifies data integration and interoperability across heterogeneous healthcare systems as the foundational barrier to widespread DT adoption; without standardized protocols and semantics for multi-source data fusion and real-time exchange, the promise of adaptive, personalized, and predictive care remains largely unrealized. Finally, we outline actionable directions including standards-aligned data models, privacy-preserving learning (for example, federated or split learning), measurable clinical validation, and workflow-aware user experience design to accelerate translation from prototypes to routine clinical practice.