Human Digital Twin Technology Using IoT In Field Of Personalised Healthcare

IoT-integrated HDT represents a revolutionary methodology for personalized medicine, allowing for real-time, data-driven simulations of patient physiology, behavior, and treatment responses. This research was necessary because proactive, individual care is increasingly required in the face of growing chronic diseases and healthcare inefficiencies-a motivation intensified by the gaps in conventional systems and the promising role of IoT-empowered HDTs in strengthening predictive analytics and ensuring better patient outcomes.

The study conducted a review of 40 peer-reviewed papers from reputed sources like IEEE, Springer, and Elsevier on the applications of HDT-IoT in healthcare during 2023-2025, following a structured multi-phase methodology. The different phases included the collection and selection of the literature, mapping 8 high-severity challenges - cybersecurity, scalability, and ML accuracy among others - comparative mapping with severity scores and sector tags, specializing in 10 papers regarding personalized healthcare, and framework development. Key findings included commonly identified IoT use cases, such as wearables-based vitals monitoring and predictive analytics for chronic diseases, and AI/ML-driven personalization, while mapping challenges that clearly indicated deployment bottlenecks such as data privacy and interoperability.

It has also advocated for a challenge-centric framework, with a modular architecture of HDT (IoT Data Layer → Semantic Engine → ML Personalization → Dashboard Interface) to reduce barriers and drive ethical and scalable solutions. Deliverables will include comparison tables, academic presentations, and wireframes that can be used practically.

This work contributes a diagnostic tool to the HDT-IoT implementations that advance personalized healthcare and reduce health disparities and policymaking. Testing through empirical pilots to validate the framework is recommended for future research.