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 envisage a roadmap for adaptive observers algorithms to address this challenge. By leveraging computational models and patient-specific measurements, adaptive observers enable the estimation of unmeasurable states while continuously adapting model parameters. Integrating adaptive observers into digital twins offers a paradigm shift: transforming them from static representations into living, evolving systems that advance personalized medicine.