INTEGRATION OF PHYSIOLOGY, DIGITAL TWIN AND ARTIFICIAL INTELLIGENCE IN SPORT: A SYSTEMATIC REVIEW, CONCEPTUAL MODEL AND THE ROLE OF BIOCHEMISTS AND PHYSIOLOGISTS
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Sep 1, 2026
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Abstract
Digital twin (DT) and artificial intelligence (AI) are becoming the foundation of modern sports engineering and medicine. The aim of this paper is to systematically review the existing literature (2021–2026) on the application of DT and AI in sport, map the role of physiologists in the development of these models, and propose a conceptual framework integrating DNA, microbiome, nutrition, psychophysiological data and the digital patient–athlete. According to the PRISMA methodology, 120 papers were identified, of which 46 were analyzed in detail after risk of bias assessment using the ROBINS-I tool [1]. The paper highlights serious methodological shortcomings in the literature, quantifies the distribution of effects, and concludes that the level of evidence is low. The role of the physiologist is crucial but systemically neglected
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Keywords
digital twin, artificial intelligence, sports physiology, athlete monitoring, personalized sports medicine
References
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18. Zhou, D., Chen, J., Wang, L., et al. (2025). Artificial intelligence in sport: A systematic review of performance analysis and injury prevention. Journal of Sports Science, 43(2), 145–162.
2. Bjelica, B., Hajder, Đ., Dragosavljević, S., Perović, T., Pržulj, R., Aksović, N., & Bubanj, S. (2025). Use of AI Technology Training on Motor Parameters: Systematic review. Sportske nauke i Zdravlje 15(1), 119-127.
3. Glebova, E., Desbordes, M., & Bodet, G. (2023). Digital venue twins: Enhancing spectator experience and event management. Sport, Business and Management: An International Journal, 13(4), 401–418.
4. Guyatt, G. H., Oxman, A. D., Vist, G. E., Kunz, R., Falck-Ytter, Y., Alonso-Coello, P., & Schünemann, H. J. (2008). GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. Bmj, 336(7650), 924-926.
5. Hliš, T., Fister, I., & Fister Jr., I. (2024). Digital twins in sport: Concepts, taxonomies, challenges and practical potentials. Expert Systems with Applications, 258, 125104.
6. Kakavas, G., Malliaropoulo, N., Mitrotasio, M., Tsaklis, P., & Bikos, G. (2022). Is virtual twins technology the future in sports medicine. Journal of Physical Medicine Rehabilitation & Disabilities, 8(1), 75.
7. Lee, C. H., Chang, M. H., & Li, Z. H. (2026). From data to prevention: A systematic review of artificial intelligence applications in sports injury prediction. Technology and Health Care, 09287329261455797.
8. Li, X., Loscalzo, J., Mahmud, A. F., Aly, D. M., Rzhetsky, A., Zitnik, M., & Benson, M. (2025). Digital twins as global learning health and disease models for preventive and personalized medicine. Genome Medicine, 17(1), 11.
9. Liu, D., Wang, H., Xiang, X., & Fang, H. (2025). Digital Twin in healthcare and medical field: A review. Pattern Recognition and Computer Vision in the New AI Era, 247-271.
10. Liu, L., Zhu, Q., Zong, Y., Chen, X., Zhang, W., & Wang, J. (2026). Machine learning prediction models for preeclampsia: systematic review and meta-analysis. Journal of Medical Internet Research, 28, e78714.
11. Lukač, L., Fister Jr., I., & Fister, I. (2022). Digital twin in sport: From an idea to realization. Applied Sciences, 12(24), 12741.
12. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Bmj, 372.
13. Padulo, J. (2025). Sport and health science: interdisciplinary approaches to modern challenges. British Medical Bulletin, 155(1), ldaf007.
14. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Bmj, 372.
15. Sterne, J. A., Hernán, M. A., Reeves, B. C., Savović, J., Berkman, N. D., Viswanathan, M., ... & Higgins, J. P. (2016). ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. Bmj, 355.
16. 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.
17. Vec, V., Tomažič, S., Kos, A., & Umek, A. (2024). Trends in real-time artificial intelligence methods in sports: a systematic review. Journal of Big Data, 11(1), 148.
18. Zhou, D., Chen, J., Wang, L., et al. (2025). Artificial intelligence in sport: A systematic review of performance analysis and injury prevention. Journal of Sports Science, 43(2), 145–162.
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Nestorović, V., & Nestorović, A. (2026). INTEGRATION OF PHYSIOLOGY, DIGITAL TWIN AND ARTIFICIAL INTELLIGENCE IN SPORT: A SYSTEMATIC REVIEW, CONCEPTUAL MODEL AND THE ROLE OF BIOCHEMISTS AND PHYSIOLOGISTS. Physical Education and Sport Through The Centuries, 13(2), 53–68. Retrieved from https://phedss.fsfv-pr.rs/index.php/phedss/article/view/79
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