Research on the Application Strategy of AI-Enabled Intelligent Management and Effectiveness Improvement in the Full Process of Sports Rehabilitation

Abstract

This study investigates the application strategies of artificial intelligence (AI) in the full process of sports rehabilitation, with the goal of realizing intelligent management and measurable effectiveness improvement. Building on deep learning algorithms and data-driven decision-making, AI techniques are integrated into multiple rehabilitation stages, including initial assessment, program design, real-time monitoring, feedback adjustment, and outcome evaluation. By combining sensor-based motion capture, wearable devices, and clinical assessment data, the study constructs intelligent evaluation and treatment models capable of tracking patients’ functional recovery trajectories and automatically updating individualized rehabilitation plans. Comparative experiments between AI-assisted and conventional rehabilitation approaches indicate that AI support can shorten functional recovery time, improve the precision of exercise prescription, and enhance patient adherence and satisfaction. Furthermore, AI-based systems facilitate more efficient allocation of medical and rehabilitation resources, support standardized yet personalized interventions, and provide clinicians with objective, continuous data for decision support. The findings demonstrate that AI enables comprehensive and refined management of sports rehabilitation processes, strengthens the scientific basis and targeting of interventions, and offers a scalable technical framework for future clinical practice. This work provides methodological references for integrating AI into rehabilitation management platforms and highlights key challenges such as data quality, model interpretability, and interdisciplinary collaboration that must be addressed to promote wider application.