Health Digital Twins and Personalized Medicine: Challenges and benefits

Digital Twins are defined as virtual models of physical objects. They are used in the industry in the last decades to optimize processes by simulating the life cycle of a product based on various data. The term “Health Digital Twins” describes a virtual representation of a patient (“physical twin”). In order for the digital twin of a patient to be generated various patient-centered, such as patient clinical data, population data, and other environmental variables could be used. The physical twin/ patient data could be used to measure and predict the response to therapeutic interventions and other lifestyle modifications. Artificial Intelligence and Internet of Things are at the heart of this technology.
In a recent article published in the NPJ Digital Medicine Coorey et al., reviews current literature on the potential application of health digital twins in cardiovascular diseases. The review discusses in depth the need for interdisciplinary approach to successfully generate digital twins that could provide information and data towards personalized therapeutic strategies for patients with CVD. However, there are key challenges that could be grouped into three categories: computational challenges, clinical implementation, and regulatory and safety frameworks.
In general, health digital twins technology combining AI driven analysis and real world data collection could be evolved to a powerful tool towards personalized healthcare.
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