Artificial Intelligence Driven Musculoskeletal Rehabilitation: Current Evidence, Clinical Translation, and a Human-in-the-Loop Framework for Functional Recovery

Abstract: Musculoskeletal disorders generate substantial global disability and require rehabilitation models that are effective, scalable, personalized, and capable of supporting recovery beyond episodic clinic visits. Artificial intelligence (AI) is increasingly incorporated into rehabilitation through machine learning, computer vision, wearable sensing, adaptive telerehabilitation, robotics, exergaming, and clinical decision support. This critical review evaluates the clinical promise and limitations of AI-enabled musculoskeletal rehabilitation and proposes a human-supervised translational framework, the Artificial Intelligence–Driven Rehabilitation Framework for Musculoskeletal Recovery (AI-DRF-MSK). Recent randomized and synthesized evidence suggests that selected AI-assisted strategies can improve pain, physical function, or range of motion. However, the evidence remains heterogeneous across diagnoses, technologies, comparators, outcome measures, and follow-up periods. In low back pain, statistically significant pooled improvements have not consistently exceeded prespecified thresholds for clinical importance, illustrating the need to distinguish statistical effects from patient-important recovery. The AI-DRF-MSK framework integrates six functions—intelligent assessment, AI-supported clinical profiling, personalized rehabilitation, real-time feedback, adaptive monitoring, and human clinical oversight—within a closed-loop model. Safety, explainability, equity, privacy, external validation, and post-deployment monitoring are treated as structural requirements. The most defensible near-term role for AI is therefore not autonomous replacement of physiotherapists, but augmentation of physiotherapist-led rehabilitation. Future research should prioritize multicenter prospective validation, clinically meaningful outcomes, long-term follow-up, transparent reporting, cost-effectiveness, workflow effects, subgroup performance, and surveillance for model drift and unintended harm. Keywords: artificial intelligence; musculoskeletal rehabilitation; physical therapy; functional recovery; digital rehabilitation; telerehabilitation; machine learning; clinical decision support. Title: Artificial Intelligence Driven Musculoskeletal Rehabilitation: Current Evidence, Clinical Translation, and a Human-in-the-Loop Framework for Functional Recovery Author: Waseem Mumtaz Ahamed International Journal of Interdisciplinary Research and Innovations ISSN 2348-1218 (print), ISSN 2348-1226 (online) Vol. 14, Issue 3, July 2026 - September 2026 Page No: 97-105 Research Publish Journals Website: www.researchpublish.com Published Date: 17-September-2026 DOI: https://doi.org/10.5281/zenodo.22812942 Paper Download Link (Source) https://www.researchpublish.com/papers/artificial-intelligence-driven-musculoskeletal-rehabilitation-current-evidence-clinical-translation-and-a-human-in-the-loop-framework-for-functional