Beyond the conventional: Artificial intelligence in identifying risk factors in sports injuries. A scoping review

To map contemporary uses of artificial intelligence (AI) to identify conventional and unconventional risk factors for sports injuries in athletes.

Following Joanna Briggs Institute guidance and PRISMA-ScR, we conducted an open-access search (January 2015-September 2025) in PubMed/MEDLINE, Scopus, and ScienceDirect using MeSH/DeCS terms for AI and injury risk. Records were managed in Rayyan with de-duplication, independent triple screening, standardized data extraction, and narrative synthesis to address heterogeneity.

Fifty-nine studies met inclusion criteria. Frequently modelled predictors included sleep quality and psychological state, external and internal load metrics, and prior injury. AI approaches spanned classical machine learning and deep learning applied to multimodal sensor and clinical data. Across studies, generalizability was limited by heterogeneous populations, outcomes, and measurement/reporting practices; few works reported consistent measurement standards or external validation. Where assessed, observer agreement and classification performance were acceptable but variable.

AI for sports-injury risk is expanding rapidly, led by classical machine learning on multimodal sensor data. Key gaps are external validity and reproducibility. Progress will require multicenter prospective cohorts, standardized measurement and reporting, sport-specific meta-analyses, transparent model sharing, and deliberate clinical integration into athlete care pathways.