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The integration of artificial intelligence (AI) and wearable technologies has reshaped contemporary sport practice by enabling continuous, multidimensional athlete monitoring. Wearable systems generate high-frequency physiological, biomechanical, and behavioral data; however, meaningful interpretation of these datasets requires advanced analytical approaches. This mini review synthesizes current evidence on the combined application of AI and wearable technologies in sport, with emphasis on performance optimisation, injury risk estimation, return-to-play decision support, and athlete wellbeing. The literature indicates that AI-driven models can enhance individualized training prescription, improve workload regulation, and support early identification of maladaptive patterns. Nevertheless, predictive accuracy and practical utility remain highly dependent on data quality, model validation, contextual interpretation, and practitioner expertise. Ethical considerations, including data privacy, algorithm transparency, and responsible governance, represent additional challenges for widespread implementation. Overall, the findings support a human-in-the-loop framework in which AI functions as an advanced decision-support tool rather than an autonomous authority. When applied within structured and context-aware practice models, AI-integrated wearable systems may contribute to more adaptive, individualized, and sustainable athlete management strategies.
Alkasasbeh et al. (Fri,) studied this question.