From injury to comeback: A systematic review of machine learning models predicting return to sport in athletes.

Objective: This study aims to systematically review the current literature on the application of machine learning to predict return-to-sport (RTS) decisions after athletic injuries. The review focuses on identifying the types of machine learning models used, the commonly used predictive variables, and the methodological characteristics and limitations between studies in terms of design, model development, evaluation, and reporting. Method: A comprehensive literature search was conducted on 1 May 2025 in three electronic databases: Web of Science, PubMed, and SPORTDiscus (EBSCO). Two independent reviewers selected the retrieved studies based on predefined inclusion and exclusion criteria. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias in the included prognostic modeling studies. Results: Of the 56 studies initially identified, 11 met the inclusion and exclusion criteria. Knee injuries were the most frequently modeled injury type for RTS decision-making (n = 4). The area under the receiver operating characteristic curve (ROC AUC) was the most commonly reported performance metric, presented in 82% of the included studies. Random Forest (RF) was the most widely used machine learning algorithm, applied in six studies (55%), and demonstrated the best predictive performance in four of them, with two studies reporting an AUC greater than 0.9. Some studies employed feature importance analysis or interpretability methods (e.g. SHAP) to identify key predictive variables. However, challenges remain in translating these models into clinical practice. Conclusions: Machine learning techniques demonstrate promising potential for predicting RTS in athletes. Nevertheless, substantial heterogeneity across studies-particularly in RTS definitions, feature selection, and model development which limits the generalizability and clinical applicability of current models.