Journal of evaluation in clinical practice · 2026
The Digital Transformation of Rehabilitation Medicine: A Narrative Review of Artificial Intelligence Innovations, Clinical Integration, and Future Paradigms.
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Wu Hao, Yang Yali, Wang Li, Su Hao, Li Xiaobing
Аннотация
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.
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.