Building smarter surveillance: a framework for artificial intelligence-enabled monitoring of overuse injuries in youth sports

Early sport specialization (ESS), defined as intensive, year-round training in a single sport at the exclusion of others, has become increasingly prevalent among youth athletes. This trend has coincided with a rise in overuse injuries, and premature attrition. Concurrently, advances in wearable sensors and artificial intelligence (AI) enable real-time quantification of training load, biomechanical deviations, and recovery. However, a translational gap remains: existing AI surveillance models are predominantly validated in elite adult male cohorts, leaving youth athletes underserved. To address this gap, we propose a translational four-pillar framework for ESS-aware AI injury monitoring in youth sport. Bridging technological capabilities with developmental physiology through structured AI surveillance provides a structured risk-monitoring roadmap with the potential to inform preventative strategies, optimize physical readiness, and support long-term musculoskeletal health in youth athletes.