From data to prevention: A systematic review of artificial intelligence applications in sports injury prediction

Abstract

Purpose

Artificial intelligence (AI) shows considerable potential for sports injury prediction, yet a comprehensive methodological review of its empirical applications remains limited. This study aimed to systematically review the empirical literature on the use of AI and machine learning (ML) for sports injury prediction.

Methods

Following the PRISMA 2020 guidelines, a systematic search was conducted across PubMed, IEEE Xplore, SPORTDiscus, Web of Science, and Scopus for literature published between January 2015 and March 2026. In addition to the structured database search, a small number of database-recommended articles identified through platform recommendation functions were also screened for eligibility. After a multi-stage screening process, 18 empirical studies were included in the final qualitative synthesis.

Results

Risk of bias assessment using PROBAST indicated that only one study (5.6%) had a low overall risk of bias, whereas most studies were judged to be high risk, primarily because of weaknesses in the analysis domain and reliance on internal validation. Across the included studies, AI-based models demonstrated potential for handling multidimensional training load, physiological, biomechanical, and psychological data; however, most prediction models relied exclusively on internal validation, limiting confidence in their generalizability.

Conclusion

AI demonstrates clear promise for sports injury prediction, but the current evidence base remains constrained by limited external validation, inconsistent reporting practices, and the continued overrepresentation of male cohorts. Future research should prioritize methodological rigor, broader geographic and demographic representation, standardized reporting, and explainable AI (XAI) approaches to enhance the trustworthiness and practical utility of these models for coaches and clinicians.