AI and Digital Twins for Personalized Physiotherapy Simulations

Healthcare is one of the sectors that has rapidly evolved with the development of artificial intelligence (AI) and digital twin technologies, enabling more individualized, predictive and adaptive treatment approaches. The implementation of digital twins – virtual models of a patient's musculoskeletal or neurological system – offers the possibility of continuously simulating rehabilitation scenarios. This chapter discusses the convergence of AI and digital twin models in enabling personalized, simulation-based physiotherapy. It outlines the technical foundations of digital twin construction, including the integration of multimodal data from medical imaging, wearable sensors and motion-capture systems. The chapter also explains how adaptive decision-making within these virtual environments can be achieved using AI techniques such as machine learning (ML), deep learning and reinforcement learning. ML algorithms are widely used to forecast recovery trajectories and classify patients into functional subgroups.