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The integration of Artificial Intelligence (AI) is catalyzing a paradigm shift from \u201cexperience\u2010driven\u201d to \u201cdata\u2010driven\u201d precision rehabilitation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This narrative review delineates the current landscape of AI innovations in rehabilitation, evaluates their clinical integration across the patient lifecycle, and identifies the socio\u2010technical barriers to widespread adoption.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We narratively synthesized recent advancements in four foundational technological pillars: Computer Vision (CV) for markerless motion capture, Reinforcement Learning (RL) for intention\u2010aware robotics, Digital Twins (DT) for prognostic simulation, and Explainable AI (XAI) for clinical decision support.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our analysis reveals that AI\u2010driven models enhance rehabilitative efficiency by providing highly objective functional assessments, demonstrating high accuracy in specific controlled validation datasets. Clinical evidence suggests that AI\u2010integrated interventions can potentially reduce certain motor recovery cycles by up to 20%\u201330% through real\u2010time assist\u2010as\u2010needed (AAN) paradigms. Furthermore, the deployment of AI\u2010mediated remote monitoring and virtual assistants has demonstrated up to a 25% relative improvement in patient adherence post\u2010discharge based on selected pilot studies, effectively bridging the \u201crehabilitation gap\u201d between hospital and home.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>While AI offers transformative potential for personalized and accessible care, its maturation depends on overcoming challenges related to data heterogeneity, algorithmic \u201cblack\u2010box\u201d distrust, and systemic interoperability. We propose a multidisciplinary roadmap to establish unified regulatory frameworks and standardized APIs. Ultimately, the transition to AI\u2010augmented rehabilitation is highly promising for achieving equitable and evidence\u2010based functional recovery in the era of digital medicine.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jep.70580","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T12:55:08Z","timestamp":1788180908000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The Digital Transformation of Rehabilitation Medicine: A Narrative Review of Artificial Intelligence Innovations, Clinical Integration, and Future Paradigms"],"prefix":"10.1111","volume":"32","author":[{"given":"Hao","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Rehabilitation Medicine Bazhong Central Hospital Bazhong Sichuan 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