The Digital Transformation of Rehabilitation Medicine: A Narrative Review of Artificial Intelligence Innovations, Clinical Integration, and Future Paradigms - Hao Wu; Yali Yang; Li Wang; Hao Su; Xiaobing Li (Journal of Evaluation in Clinical Practice, 2026)
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 selected pilot studies, effectively bridging the “rehabilitation gap” between hospital and home. Conclusion While AI offers transformative potential for personalized and accessible care, its maturation depends on overcoming challenges related to data heterogeneity, algorithmic “black‐box” distrust, and systemic interoperability. We propose a multidisciplinary roadmap to establish unified regulatory frameworks and standardized APIs. Ultimately, the transition to AI‐augmented rehabilitation is highly promising for achieving equitable and evidence‐based functional recovery in the era of digital medicine.