Models in Medicine: the Digital Twin for Health
DOI:
https://doi.org/10.22370/sst.2025.10.4872Keywords:
representation, prediction, mathematical, in silico, biomedical engineeringAbstract
This paper explores the role of models in medicine with a focus on the evolution and application of mathematical and computational models, particularly Digital Twins (DTs). Models in science serve as essential tools for representing complex systems and phenomena, enabling researchers to predict outcomes and develop e↵ective strategies. Historically, models have been integral to advancements in various medical fields, from pharmacokinetics to disease spread and tumor response to certain treatments. In modern medicine, predictive models, including DTs, o↵er transformative potential by improving patient outcomes through personalized medicine, optimizing healthcare management, and enhancing biomedical research. Digital Twins, which are detailed digital replicas of physical entities, are emerging as critical tools in healthcare, capable of simulating everything from individual organs to entire hospital systems. Despite their promise, the implementation of DTs faces challenges such as data privacy, integration, and modelaccuracy. Overcoming these obstacles requires collaboration among healthcare providers, researchers, and technology developers. As the field advances, these models are poised to significantly reshape the future of medical science.
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