ADVANCE APPLICATION OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN DOMAIN OF ORTHOPEDIC PHYSIOTHERAPY PRACTICE AND REHABILITATION SCIENCE

Abstract

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into orthopedic physiotherapy is transforming traditional rehabilitation paradigms by enabling personalized, data-driven, and scalable care. This paper provides a comprehensive synthesis of recent advances in AI/ML applications, including computer vision for movement analysis, wearable sensor fusion for real-time biofeedback, predictive analytics for recovery trajectory modeling, and adaptive robotic systems powered by reinforcement learning, within the domain of musculoskeletal rehabilitation. Drawing on evidence from 2019 to 2025, we critically evaluate the clinical efficacy, technical robustness, and implementation challenges of these technologies across diverse settings, with particular attention to their potential to enhance functional outcomes, patient adherence, and therapist decision-making in conditions such as osteoarthritis, post-total joint arthroplasty, and rotator cuff injuries. Despite promising proof-of-concept studies, significant gaps remain in clinical validation, algorithmic transparency, and equitable deployment, especially in low-resource and non-Western contexts. We identify key barriers, including limited therapist-AI collaboration frameworks, insufficient focus on patient-centered outcomes, and ethical concerns around data privacy and algorithmic bias. To address these challenges, we propose an integrative, human-centered implementation model grounded in the Consolidated Framework for Implementation Research (CFIR) and aligned with the World Health Organization's global rehabilitation priorities. Our analysis underscores the need for interdisciplinary collaboration, context-adaptive design, and rigorous randomized trials to translate AI innovations into sustainable, equitable, and clinically meaningful tools that augment, not replace, the therapeutic alliance at the heart of physiotherapy practice.