Artificial intelligence and machine learning in sports medicine: mapping clinical tasks and assessing clinical maturity - a scoping review

BACKGROUND: Artificial intelligence (AI) and machine learning (ML) are rapidly transforming the medical field. The aim of this review was to outline the current scientific state of AI and ML application in sports medicine, evaluate the level of clinical validation and readiness for implementation, and identify key priorities to guide future advancements and implementation into injury risk assessment, diagnosis, rehabilitation and clinical decision-making in sport medicine. METHODS: A scoping review was conducted with a literature search performed on February 5, 2026, using the MEDLINE, EMBASE and Web of Science databases which targeted AI or ML application on individuals within a sports medicine context. RESULTS: Of 8,677 studies, 97 studies were included. Most research covered orthopaedics (70.1%) and neurology (18.6%), where AI was applied for injury prediction, diagnostic image analysis, and recovery estimation. Predictive and estimation models were the dominant application (57.7%). Reported discriminative performance was frequently high. However, the majority of studies relied on retrospective datasets and internal validation. Calibration reporting was uncommon, and prospective workflow integration was rare, with a single study attempting an interventional prevention strategy. Substantial heterogeneity in modelling approaches, data inputs, and outcomes definitions was observed. CONCLUSION: Although AI and ML applications in sports medicine frequently demonstrate strong within-sample performance, most remain in early-stage development. Currently, these tools should be viewed as supportive adjuncts rather than autonomous decision-making systems. KEY TERMS: AI, Predictive modeling, Diagnostic imaging, Rehabilitation, Deep learning, Return to sport.