Machine Learning, AI, and Robotics in Musculoskeletal Rehabilitation

Musculoskeletal (MSK) conditions drive a large share of global pain, disability, and lost productivity. Rehabilitation can be effective, but outcomes vary across individuals due to differences in diagnosis, movement patterns, comorbidities, adherence, and access to care. Meanwhile, clinics face rising demand and limited time for detailed assessment and follow-up. This Research Topic will address how machine learning (ML), artificial intelligence (AI), and robotics can improve MSK rehabilitation by enabling precise measurement, prediction, and personalization across the care pathway. We aim to curate robust, clinically meaningful contributions that move beyond proof-of-concept and demonstrate validity, safety, usability, and impact in real-world settings. We especially welcome work that supports clinical decisions, quantifies function outside the clinic, adapts rehabilitation dose and progression over time, and improves access to high-quality rehabilitation through intelligent systems.Recent growth in wearable sensors, smartphone-based tools, computer vision, and electronic health data has expanded the ability to measure movement and function at scale. In parallel, AI methods now support pattern recognition, trajectory modeling, and decision support that can be integrated into clinical workflows. Robotics and intelligent assistive devices (including exoskeletons and soft robotics) can deliver repeatable training, adaptive assistance, and objective performance metrics. Despite these advances, translation into practice remains limited by small datasets, unclear generalizability, limited external validation, bias, insufficient interpretability, and a mismatch between technical metrics and clinical outcomes. A focused collection is timely to consolidate best practices and accelerate clinically grounded innovation.This Research Topic welcomes submissions on ML/AI and robotic approaches that advance assessment, prognosis, treatment planning, and delivery of MSK rehabilitation (e.g., spine, shoulder, hip, knee, ankle/foot; post-operative rehab; sports injuries; chronic MSK pain). Relevant themes include: -AI-based movement assessment (e.g., wearables/IMUs, EMG, force sensing, video/computer vision)-Digital biomarkers and remote monitoring-Prediction of recovery and treatment response-Adaptive exercise prescription (personalized dosing and progression)-Adherence and engagement modeling-Robotic rehabilitation and assistive devices with human-in-the-loop control-Rigorous clinical evaluation and external validation-Transparent reporting-Bias and fairness assessment-Interpretable modeling-Implementation studies (workflow integration, acceptability, safety, cost, scalability)Article types include Original Research, Systematic Reviews, Methods, Brief Research Reports, Clinical Trials, and Perspectives. Topic editors: Stefania Spina, M. Hongchul Sohn, Jongsang Son Source: https://www.frontiersin.org/research-topics/76982/machine-learning-ai-and-robotics-in-musculoskeletal-rehabilitation