Digital Twin in Healthcare: A Review of Technologies, Applications and Challenges

A digital twin is a virtual replica of a physical entity that is continuously updated with real-world data and can be used to analyse, predict and test outcomes without risk to the original. Originally developed for aerospace and manufacturing, the concept is now being adapted to healthcare, where the twin may represent a single organ, an entire patient or a hospital. Conventional care relies on population averages, periodic check-ups and trial-and-error treatment, which leaves gaps in personalisation, early detection and safety. This paper reviews the concept of the healthcare digital twin, its enabling technologies (wearable sensors, artificial intelligence, cloud computing, medical imaging and health records, and 5G networks), a generic working architecture, and major application areas including personalised medicine, surgical planning, chronic disease management, drug development and hospital operations. It further discusses live patient monitoring, benefits, data privacy and regulatory considerations (HIPAA, GDPR and India’s DPDP Act 2023), and open challenges of cost, interoperability, workforce readiness and biological complexity. The review concludes that digital twins can make healthcare more predictive, personalised and safer, provided that privacy, standardisation and affordability are addressed.