Artificial Intelligence in Sport: Advances, Challenges and Applications

Sports injury prevention remains a major challenge in both elite and recreational sports. Injuries have substantial consequences for athletes' health, performance, quality of life, and career longevity, while also imposing significant medical and economic burdens. Despite considerable advances in sports medicine, biomechanics, and exercise science, the early identification of injury risk factors and the implementation of effective preventive strategies remain difficult because sports injuries result from complex interactions among physiological, biomechanical, environmental, and behavioral factors.Recent advances in Artificial Intelligence (AI) are opening unprecedented opportunities to improve the understanding, prediction, and prevention of sports injuries. Developments in machine learning, deep learning, computer vision, multimodal data analytics, and wearable technologies now enable the integration of large and heterogeneous datasets, including training load, biomechanical parameters, physiological measurements, video recordings, wearable sensor data, medical history, and environmental information. By leveraging these diverse sources of information, AI-driven models can identify injury-related patterns, detect early signs of fatigue or overload, and support personalized injury prevention strategies. Beyond injury risk prediction, AI is transforming sports practice through automated movement analysis, biomechanical assessment, training-load monitoring, recovery surveillance, clinical decision support, and return-to-sport planning following injury. These technologies enable more objective, data-driven, and individualized decision-making while contributing to both athlete safety and performance optimization. However, the widespread adoption of AI in sports injury prevention also raises important scientific and methodological challenges. Data quality, interoperability, model interpretability, clinical validation, algorithm generalizability, ethical considerations, privacy protection, and regulatory compliance remain critical issues that require close collaboration among experts in artificial intelligence, sports science, biomechanics, medicine, rehabilitation, engineering, and data science.This Special Issue aims to provide an interdisciplinary forum for the dissemination of cutting-edge research on Artificial Intelligence applied to sports injury prevention. We invite original research articles, systematic reviews, meta-analyses, methodological contributions, case studies, and perspective papers that advance both the scientific understanding and practical implementation of AI-based approaches in this rapidly evolving field.Topics of interest include, but are not limited to:• Artificial intelligence models for injury risk prediction;• Machine learning and deep learning applications in sports science;• Computer vision and automated human movement analysis;• Biomechanics and multimodal data fusion;• Wearable technologies and intelligent monitoring of training load;• Detection of fatigue, overtraining, and injury risk factors;• Clinical and coaching decision-support systems;• Explainable Artificial Intelligence (XAI) for sports medicine;• AI-assisted rehabilitation and return-to-sport strategies;Ethical, legal, and regulatory aspects of AI in sports medicine and athlete monitoring.By bringing together researchers from multiple disciplines, this Special Issue seeks to stimulate scientific dialogue on the opportunities, challenges, and future directions of Artificial Intelligence for sports injury prevention. Ultimately, it aims to foster innovative, evidence-based, and clinically relevant solutions that contribute to healthier athletes, safer sports participation, and enhanced athletic performance. Topic editors: Philippe GORCE, Emma Fortune, Julien Jacquier-Bret Source: https://www.frontiersin.org/research-topics/84948/artificial-intelligence-in-sport-advances-challenges-and-applications