Application of Artificial Intelligence for Predicting Sports Injuries and Customizing Personalized Prevention Strategies: A Scoping Review
8 affiliations
- 1Research Unit “Sport Sciences, Health and Movement”, High Institute of Sports and Physical Education of Kef, University of Jendouba, Kef 7100, Tunisia
- 2Training Department, Police College, Qatar Police Academy, Doha 7157, Qatar
- 3Faculty of Sports Sciences, Atatürk University, Erzurum 25240, Türkiye
- 4Heart Failure Research Laboratory (LR12SP09), Farhat HACHED Hospital, Faculty of Medicine of Sousse, University of Sousse, Sousse 4054, Tunisia
- 5Naufar Center, Doha P.O. Box 93097, Qatar
- 6Research Institute for Sport and Exercise, University of Canberra, Canberra, ACT 2601, Australia
- 7Division of Cardiology and Angiology, University Hospital Magdeburg, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany
- 8Institut Supérieur de Sport et de l’Education Physique du Kef, Université de Jendouba, Le Kef 7100, Tunisia
Abstract
Background: Sports injuries impose a substantial burden on athletes. Machine learning (ML) and deep learning (DL) methods, collectively referred to as artificial intelligence (AI), are increasingly applied to develop predictive models and targeted prevention strategies. Objective: This scoping review aimed to map contemporary trends in AI applications for sports injury prediction and personalised prevention strategies, critically appraising the existing methodological approaches and identifying future research directions. Methods: Following PRISMA-ScR guidelines, we systematically searched five electronic databases, i.e., PubMed, Web of Science, Institute of Electrical and Electronics Engineers Xplore, Scopus, and Google Scholar, for peer-reviewed studies published up to February 2026 that applied AI methods for injury prediction and/or prevention in athletic populations. Results: Thirty-nine studies were included. Tree-based ML algorithms were the most common (59% of studies) methods used, with reported area under the curve values ranging from 0.82 to 0.95. DL was used in 18% of studies, with one hybrid model reporting 92% accuracy. Integrating multi-modal data was associated with improved model performance in 37% of studies. Among included studies, AI-informed prevention strategies were associated with injury reductions ranging from 23% to 42%, derived from synthesis-level and single-centre intervention evidence, respectively. The key challenges identified were heterogeneous injury definitions, small sample sizes, and data privacy concerns. Conclusions: AI models can inform personalised injury prevention, but their clinical use is limited by methodological issues. Key limitations include heterogeneous injury definitions, small sample sizes, and a lack of external validation. Standardised protocols are needed to improve the reliability and application of these models in practice.
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BibTeX
@article{dhahbi2026application,
title = {{Application of Artificial Intelligence for Predicting Sports Injuries and Customizing Personalized Prevention Strategies: A Scoping Review}},
author = {Wissem Dhahbi and Nidhal Jebabli and Marouen Souaifi and Halil İ̇brahim Ceylan and Helmi Ben Saad and Karim Chamari and David B. Pyne and Hélmi Chaabène},
journal = {Bioengineering},
year = {2026},
month = jun,
doi = {10.3390/bioengineering13060692},
url = {https://doi.org/10.3390/bioengineering13060692},
note = {CC BY},
}
RIS
TY - JOUR
TI - Application of Artificial Intelligence for Predicting Sports Injuries and Customizing Personalized Prevention Strategies: A Scoping Review
AU - Wissem Dhahbi
AU - Nidhal Jebabli
AU - Marouen Souaifi
AU - Halil İ̇brahim Ceylan
AU - Helmi Ben Saad
AU - Karim Chamari
AU - David B. Pyne
AU - Hélmi Chaabène
PY - 2026
DA - 2026/06/17
JO - Bioengineering
DO - 10.3390/bioengineering13060692
UR - https://doi.org/10.3390/bioengineering13060692
AB - Background: Sports injuries impose a substantial burden on athletes. Machine learning (ML) and deep learning (DL) methods, collectively referred to as artificial intelligence (AI), are increasingly applied to develop predictive models and targeted prevention strategies. Objective: This scoping review aimed to map contemporary trends in AI applications for sports injury prediction and personalised prevention strategies, critically appraising the existing methodological approaches and identifying future research directions. Methods: Following PRISMA-ScR guidelines, we systematically searched five electronic databases, i.e., PubMed, Web of Science, Institute of Electrical and Electronics Engineers Xplore, Scopus, and Google Scholar, for peer-reviewed studies published up to February 2026 that applied AI methods for injury prediction and/or prevention in athletic populations. Results: Thirty-nine studies were included. Tree-based ML algorithms were the most common (59% of studies) methods used, with reported area under the curve values ranging from 0.82 to 0.95. DL was used in 18% of studies, with one hybrid model reporting 92% accuracy. Integrating multi-modal data was associated with improved model performance in 37% of studies. Among included studies, AI-informed prevention strategies were associated with injury reductions ranging from 23% to 42%, derived from synthesis-level and single-centre intervention evidence, respectively. The key challenges identified were heterogeneous injury definitions, small sample sizes, and data privacy concerns. Conclusions: AI models can inform personalised injury prevention, but their clinical use is limited by methodological issues. Key limitations include heterogeneous injury definitions, small sample sizes, and a lack of external validation. Standardised protocols are needed to improve the reliability and application of these models in practice.
SN - 2306-5354
ER -