Medical Digital Twins for Personalized Chronic Care

This work introduces the concept of Patient Medical Digital Twins (PMDTs) to simulate treatment outcomes, optimize drug dosages, and deliver personalized chronic care. The PMDT model, supported by an interconnected ecosystem, is validated iteratively by medical institutions to ensure its efficacy and applicability. At its core, the PMDT leverages expressive knowledge structures to capture a patient’s psychosomatic, cognitive, biometric, and genetic data, creating a comprehensive personal digital footprint. This enables medical professionals to run simulations predicting health issues over time and to proactively implement personalized preventive interventions. The PMDT ecosystem integrates big data analytics, continuous monitoring, cognitive simulation, and AI technologies. By connecting stakeholders across the care continuum, it provides deeper insights into a patient’s medical history and supports informed, shared decision-making. Validated in a pilot study through an EU-funded healthcare initiative, the PMDT demonstrates its transformative potential at the intersection of Big Data and AI, positioning itself as a critical tool for advancing personalized preventive care.