Iterative calibration of medical digital twins via adaptive estimators

Abstract

While iterative calibration of computational models is a fundamental aspect of digital twins, it has been largely overlooked. Instead of focusing on parameter identification for static models, the implementation of digital twins requires not only high-resolution computational models but also the ability to assimilate patient-specific data continuously. Here, we review adaptive estimator algorithms and how this can address iterative calibration of digital twins, enabling the estimation of unmeasurable states while continuously adapting model parameters. Integrating adaptive estimators into digital twins offers a paradigm shift: transforming them from static representations into living, evolving systems that advance personalized medicine.