AI-POWERED REHABILITATION FOR MUSCULOSKELETAL DISORDERS: A SUSTAINABLE APPROACH TO BACK PAIN MANAGEMENT: NARRATIVE REVIEW

Abstract

Background: Low back pain (LBP is one of the leading causes of disability worldwide and poses a substantial burden on healthcare systems, workplace productivity, and overall quality of life. Although conventional rehabilitation approaches are effective, their real-world impact is often limited by challenges related to accessibility, scalability, cost, and long-term adherence. Recent advances in artificial intelligence (AI) and mobile health technologies have introduced new opportunities to address these limitations and reshape musculoskeletal rehabilitation. Objective: This narrative review aims to explore the evolving role of artificial intelligence in the rehabilitation of low back pain, with a focus on its clinical applications, effectiveness, sustainability, and implications for future care delivery. Main Discussion Points: The review synthesizes current evidence on AI-driven diagnostics, decision support systems, and AI-enabled telerehabilitation platforms. Key themes include personalized exercise prescription, real-time feedback through wearable sensors and mobile applications, patient engagement strategies, cost-effectiveness, and equity in access to care. The integration of AI into hybrid rehabilitation models that combine digital self-management with clinician oversight is highlighted as a particularly promising approach. Persistent challenges such as data privacy concerns, algorithmic bias, digital disparities, and variability in outcome measures are also discussed. Conclusion: AI-supported rehabilitation represents a promising advancement in low back pain management, offering scalable, personalized, and potentially cost-effective solutions. While existing evidence supports its clinical potential, further high-quality, long-term research and robust regulatory frameworks are required to ensure safe, equitable, and sustainable implementation.

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