Artificial Intelligence in Sports Science: A Systematic Review on Performance Optimization, Injury Prevention, and Rehabilitation

Maheshkumar Baladaniya 1, Arbind Kumar Choudhary 2 *
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1 Department of Physical Therapy, Neighborhood Physical Therapy PC, New City, USA
2 Department of Pharmacology, Government Erode Medical College and Hospital, Tamil Nadu, India
* Corresponding Author
J CLIN MED KAZ, Volume 22, Issue 3, pp. 64-72. https://doi.org/10.23950/jcmk/16412
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Author Contributions: Conceptualization, M. B.; methodology, M. B., A. K. C.; validation, not applicable; formal analysis, M. D.; literature review, M. D.; resources, not applicable; data curation, M. D.; writing – original draft preparation, M. D.; expertise in physical therapy and rehabilitation, M. D.;writing – review and editing, A. K. C.; pharmacological insights, A. K. C.; visualization, M. D.; supervision, A. K.; project administration, not applicable; funding acquisition, not applicable; final approval, A. K. C.. All authors have read and agreed to the published version of the manuscript.

Data availability statement: All data analyzed in this review are derived from published articles included in the systematic review. The full list of included studies and extracted data are available from the corresponding author upon reasonable request.

ABSTRACT

Background: Artificial intelligence (AI) is quickly revolutionizing sports science, providing researchers and practitioners with new means to support the optimization of performance, the improvement of rehabilitation, and the prevention of injuries. Although many AI interventions have been tested in sports, there is still insufficient methodologically sound evidence on the effectiveness and feasibility of AI to support and monitoring in the sport context.
Objective: The purpose of this systematic review and meta-analysis was to pool the existed studies to investigate the effects of AI-based interventions on sport performance, injury prevention and rehabilitation in human participants.
Methods: A comprehensive search of five databases (PubMed, Scopus, Web of Science, IEEE Xplore, SPORTDiscus) was conducted for studies published from January 2015 to December 2024. Papers based on human subjects and reporting AI-based training or rehabilitation outcomes in sports/games were considered. Quality of the studies was determined using the Cochrane RoB 2.0 tool and Newcastle-Ottawa Scale. Random-effects meta-analysis was conducted where the effect size was for SMD, and the I² statistic was used for heterogeneity. Sensitivity and publication bias tests were also performed.
Results: There were 19 studies included in total, 17 of which could be used for meta-analysis. The meta-analysis demonstrated a significant and moderate-to-large effect of the AI interventions on the outcomes (SMD = 0.68, 95% CI: 0.52–0.84, p < 0.001). The subgroup analysis demonstrated superior effectiveness in injury prevention (SMD: 0.75) and rehabilitation (SMD: 0.69), and the machine learning methods were more effective than other AI modalities. There was moderate heterogeneity (I² = 58%). Sensitivity analysis verified that the results were robust, and Egger’s test revealed no obvious publication bias (p = 0.23).
Conclusion: Applications of AI in sports AI interventions have significant potential to go a long way to increase performance of sports personnel, reduce risks or injuries and support sports rehabilitation. This work has implications for integrating sport performance and clinical practice with AI-based technologies. Standards for outcomes, methodological rigour, and ethical and pragmatic consideration of AI within sport participation are recommended for future research.
Keywords: Artificial intelligence, Sports science, Injury prevention, Rehabilitation, Machine learning, Meta-analysis.

CITATION

Baladaniya M, Choudhary AK. Artificial Intelligence in Sports Science: A Systematic Review on Performance Optimization, Injury Prevention, and Rehabilitation. J CLIN MED KAZ. 2025;22(3):64-72. https://doi.org/10.23950/jcmk/16412

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