Abstract

rtificial intelligence is increasingly used in sports science, sports medicine, athlete monitoring, physical education, biomechanics, coaching, and performance analysis. However, many studies report technical success without demonstrating whether an approach is transportable beyond the development setting, useful in professional practice, or adequately protective of athlete interests. This systematic scoping review examined peer-reviewed research published from January 2016 to May 1, 2026 on machine learning, deep learning, computer vision, wearable and multimodal analytics, large language models, generative artificial intelligence, and athlete digital twins. Six bibliographic databases were searched, with Google Scholar and citation tracking used as supplementary discovery methods. The search identified 1,132 records; 901 records underwent title and abstract screening, 287 reports were assessed for eligibility, and 32 publications were included. Primary empirical and technical evidence was examined separately from review, conceptual, perspective, and governance literature. Ten fully verified primary empirical studies enabled direct study-level comparison. The strongest evidence came from narrowly defined tasks with explicit reference standards, including motor-skill assessment, biomechanical estimation, markerless motion capture, and athlete tracking. Evidence for injury prediction, wearable systems, generative AI, and athlete digital twins was less mature. Recurring limitations included small or homogeneous samples, single-dataset validation, limited calibration, incomplete reporting of model behaviour, restricted reproducibility, and a lack of prospective or independent evaluation. None of the included publications demonstrated sustained decision impact or attributable improvement in athlete, educational, sports-medicine, or organisational outcomes. Progress will require temporal and external validation, calibrated uncertainty, transparent reporting, representative datasets, interoperable multimodal systems, human oversight, and safeguards for athlete autonomy, privacy, fairness, and contestability.

Keywords

Sports Analytics, Machine Learning, Athlete Monitoring, Computer Vision, Digital Twins, Generative AI, Implementation, Responsible AI,

References

  1. Adlou, B., Wilburn, C., Weimar, W. (2025). Motion capture technologies for athletic performance enhancement and injury risk assessment: A review for multi-sport organizations. Sensors, 25(14), 4384.
  2. Boillet, A., Messonnier, L.A., Cohen, C. (2024). Individualized physiology-based digital twin model for sports performance prediction: a reinterpretation of the Margaria–Morton model. Scientific Reports, 14(1), 5470.
  3. Boillet, A., Messonnier, L.A., Cohen, C. (2025). Energetic parameters of rowing performance: will the distance change in the 2028 Los Angeles Olympic Games matter?. Medicine & Science in Sports & Exercise, 57(10), 2294-2306.
  4. Chidambaram, S., Maheswaran, Y., Patel, K., Sounderajah, V., Hashimoto, D.A., Seastedt, K.P., McGregor, A.H., Markar, S.R., Darzi, A. (2022). Using artificial intelligence-enhanced sensing and wearable technology in sports medicine and performance optimisation. Sensors, 22(18), 6920.
  5. Claudino, J.G., Capanema, D.D.O., de Souza, T.V., Serrão, J.C., Machado Pereira, A.C., Nassis, G.P. (2019). Current approaches to the use of artificial intelligence for injury risk assessment and performance prediction in team sports: A systematic review. Sports Medicine - Open, 5(1), 28.
  6. Dergaa, I., Saad, H.B., El Omri, A., Glenn, J., Clark, C., Washif, J., Guelmami, N., Hammouda, O., Al-Horani, R., Reynoso-Sánchez, L., Romdhani, M., Paineiras-Domingos, L.L., Vancini, R.L., Taheri, M., Mataruna-Dos-Santos, L.J., Trabelsi, K., Chtourou, H., Zghibi M., Eken, Ö., Swed, S., Aissa, M.B., Shawki, H.H., El-Seedi, H.R., Mujika, I., Seiler, S., Zmijewski, P., Pyne, D.B., Knechtle, B., Asif, I.M., Drezner, J.A., Sandbakk, Ø, Chamari, K. (2024). Using artificial intelligence for exercise prescription in personalised health promotion: A critical evaluation of OpenAI’s GPT-4 model. Biology of Sport, 41(2), 221–241.
  7. Dolson, C.M., Harlow, E.R., Phelan, D.M., Gabbett, T.J., Gaal, B., McMellen, C., Geletka, B.J., Calcei, J.G., Voos, J.E., Seshadri, D.R. (2022). Wearable sensor-based estimation of core body temperature during exercise: A systematic review. Sensors, 22(19), 7639.
  8. Fister Jr, I., Salcedo-Sanz, S., Iglesias, A., Fister, D., Gálvez, A., Fister, I. (2021). New perspectives in the development of the artificial sport trainer. Applied Sciences, 11(23), 11452.
  9. Gámez Díaz, R., Yu, Q., Ding, Y., Laamarti, F., El Saddik, A. (2020). Digital twin coaching for physical activities: A survey. Sensors, 20(20), 5936.
  10. Gao, Y. (2025). The role of artificial intelligence in enhancing sports education and public health in higher education: Innovations in teaching models, evaluation systems, and personalized training. Frontiers in Public Health, 13, 1554911.
  11. He, Q., Chen, H., Mo, X. (2024). Practical application of interactive AI technology based on visual analysis in the professional system of physical education in universities. Heliyon, 10(3), e24627.
  12. Ishida, T., Ino, T., Yamakawa, Y., Wada, N., Koshino, Y., Samukawa, M., Kasahara, S. Tohyama, H., (2024). Estimation of vertical ground reaction force during single-leg landing using two-dimensional video images and pose estimation artificial intelligence. Physical Therapy Research, 27(1), 35-41.
  13. Jauhiainen, S., Kauppi, J.-P., Krosshaug, T., Bahr, R., Bartsch, J., Äyrämö, S. (2022). Predicting ACL injury using machine learning on data from an extensive screening test battery of 880 female elite athletes. American Journal of Sports Medicine, 50(11), 2917–2924.
  14. Johnson, W. R., Mian, A., Robinson, M. A., Verheul, J., Lloyd, D. G., & Alderson, J. A. (2020). Multidimensional ground reaction forces and moments from wearable sensor accelerations via deep learning. IEEE Transactions on Biomedical Engineering, 68(1), 289-297.
  15. Kim, J.H., Kim, J., Kang, H., Youn, B.Y. (2025). Ethical implications of artificial intelligence in sport: A systematic scoping review. Journal of Sport and Health Science, 14, 101047.
  16. Lahkar, B. K., Muller, A., Dumas, R., Reveret, L., Robert, T. (2022). Accuracy of a markerless motion capture system in estimating upper extremity kinematics during boxing. Frontiers in Sports and Active Living, 4, 939980.
  17. Ma, J., Liu, S., Pei, Y. (2025). SHAP-based interpretable machine learning for injury risk prediction in university football players: a multi-dimensional data analysis approach: J. Ma et al. Scientific Reports, 15(1), 40252.
  18. Makaruk, H., Porter, J.M., Webster, E.K., Makaruk, B., Tomaszewski, P., Nogal, M., Gawłowski, D., Sobański, Ł., Molik, B., Sadowski, J. (2025). Artificial intelligence-enhanced assessment of fundamental motor skills: validity and reliability of the FUS test for jumping rope performance. Frontiers in Artificial Intelligence, 8, 1611534.
  19. Mundt, M., Born, Z., Goldacre, M., & Alderson, J. (2022). Estimating ground reaction forces from two-dimensional pose data: a biomechanics-based comparison of alphapose, blazepose, and openpose. Sensors, 23(1), 78.
  20. Musat, C.L., Mereuta, C., Nechita, A., Tutunaru, D., Voipan, A.E., Voipan, D., Mereuta, E., Gurau, T.V., Gurău, G., Nechita, L.C. (2024). Diagnostic applications of AI in sports: a comprehensive review of injury risk prediction methods. Diagnostics, 14(22), 2516.
  21. Naughton, M., Salmon, P.M., Compton, H.R., McLean, S. (2024). Challenges and opportunities of artificial intelligence implementation within sports science and sports medicine teams. Frontiers in Sports and Active Living, 6, 1332427.
  22. Peters, M.D.J., Godfrey, C., McInerney, P., Munn, Z., Tricco, A.C., Khalil, H. (2020). Chapter 11: Scoping reviews. In JBI Manual for Evidence Synthesis.
  23. Raju, S., Singamaneni, K.K., Hooi, L.B., Rani, K.U., Chandrika, B. (2026). Machine learning framework for predicting athletic injuries and optimising performance. BMC Sports Science, Medicine and Rehabilitation, 18(1), 107.
  24. Reis, F.J.J., Alaiti, R.K., Vallio, C.S., Hespanhol, L. (2024). Artificial intelligence and machine learning approaches in sports: Concepts, applications, challenges, and future perspectives. Brazilian Journal of Physical Therapy, 28(3), 101083.
  25. Schortgen, A., Goyallon, T., Saulière, G., Muller, A., Revéret, L. (2026). Monocular markerless position tracking of elite amateur boxing fighters in real combat situation. Journal of Sports Sciences, 44(10), 1319–1333.
  26. Seshadri, D.R., Thom, M.L., Harlow, E.R., Gabbett, T.J., Geletka, B.J., Hsu, J.J., Drummond, C.K., Phelan, D.M., Voos, J.E. (2021). Wearable technology and analytics as a complementary toolkit to optimize workload and to reduce injury burden. Frontiers in Sports and Active Living, 2, 630576.
  27. Souaifi, M., Dhahbi, W., Jebabli, N., Ceylan, H.I., Boujabli, M., Muntean, R.I., Dergaa, I. (2025). Artificial intelligence in sports biomechanics: A scoping review on wearable technology, motion analysis, and injury prevention. Bioengineering, 12(8), 887.
  28. Van Eetvelde, H., Mendonça, L.D., Ley, C., Seil, R., Tischer, T. (2021). Machine learning methods in sport injury prediction and prevention: A systematic review. Journal of Experimental Orthopaedics, 8(1), 27.
  29. Vitale, J., McCall, A., Cina, A., Athlete Travel, Sleep Interest Group, van Rensburg, D. C.J., Halson, S. (2026). “Can we trust them?” An expert evaluation of large language models to provide sleep and jet lag recommendations for athletes. Sports Medicine, 56(1), 257–270.
  30. Wang, X., Wang, X., (2024). Artificial intelligence applications and teacher preparation in physical education. Frontiers in Public Health, 12, 1484848.
  31. Zhong, Q., Jiang, J., Bai, W., Yin, Z., Liao, Z., Zhong, X. (2025). Application of digital-intelligent technologies in physical education: a systematic review. Frontiers in public health, 13, 1626603.