The use of artificial intelligence for the predictive analysis of muscular injuries in athletes

Muscle injuries represent a primary cause of time-loss and significant financial burden in elite sports. Traditional prevention methods struggle to account for the multifactorial nature of injury risk, creating a need for more advanced analytical approaches. This review synthesizes the current state-of-the-art of artificial intelligence (AI) models for the predictive analysis of muscular injuries in athletes from a clinical viewpoint. A narrative literature review was conducted, involving a search of the PubMed/MEDLINE, IEEE Xplore, and Scopus databases. Key findings show that successful predictive models rely on heterogeneous data inputs from wearables (GPS), load monitoring (RPE), and clinical history. While complex algorithms like Random Forests and Neural Networks show higher predictive accuracy, significant implementation challenges persist, including data quality, the "black box" issue of model interpretability, and integration into clinical workflows. AI offers a paradigm shift towards a proactive, data-informed model of athlete care. These models are powerful tools for risk stratification that augment, rather than replace, clinical judgment, allowing medical teams to deliver highly personalized and effective injury prevention strategies