The Digital Transformation of Rehabilitation Medicine: A Narrative Review of Artificial Intelligence Innovations, Clinical Integration, and Future Paradigms
Abstract
Background: Traditional rehabilitation medicine, primarily dependent on qualitative clinical assessment and static therapeutic protocols, faces significant challenges in scalability, objectivity, and dynamic adaptability. The integration of Artificial Intelligence (AI) is catalyzing a paradigm shift from "experience-driven" to "data-driven" precision rehabilitation.
Objective: This narrative review delineates the current landscape of AI innovations in rehabilitation, evaluates their clinical integration across the patient lifecycle, and identifies the socio-technical barriers to widespread adoption.
Methods: We narratively synthesized recent advancements in four foundational technological pillars: Computer Vision (CV) for markerless motion capture, Reinforcement Learning (RL) for intention-aware robotics, Digital Twins (DT) for prognostic simulation, and Explainable AI (XAI) for clinical decision support.
Results: Our analysis reveals that AI-driven models enhance rehabilitative efficiency by providing highly objective functional assessments, demonstrating high accuracy in specific controlled validation datasets. Clinical evidence suggests that AI-integrated interventions can potentially reduce certain motor recovery cycles by up to 20%-30% through real-time assist-as-needed (AAN) paradigms. Furthermore, the deployment of AI-mediated remote monitoring and virtual assistants has demonstrated up to a 25% relative improvement in patient adherence post-discharge based on…
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