ArticleBritish journal of sports medicine2025
Machine learning approaches to injury risk prediction in sport: a scoping review with evidence synthesis.
Article in British journal of sports medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 5 of them syntheses that pooled it.
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Who cites it
49 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Machine learning models for injury risk prediction in football players: A systematic review of predictors, performance, practical applications, and limitations.The Journal of international medical research · 2026Pooled it
- The application of machine learning in the prediction of sports injuries: systematic review and meta-analysis.Scientific reports · 2026Pooled it
- Artificial intelligence in ACL injury prediction and prevention: a systematic review.Journal of orthopaedic surgery and research · 2026Pooled it
- Machine Learning Applications in Non-Contact Lower Limb Sports Injury Prediction: A Systematic Review.Journal of sports science & medicine · 2026Pooled it
- The Role of Machine Learning in Talent Identification for Team Sports: A Systematic Review.Journal of sports science & medicine · 2026Pooled it
- Article
- Machine learning models for predicting cerebrospinal fluid bacterial culture outcomes in post-neurosurgical meningitis-development and temporal three-year validation.Archives of microbiology · 2026Article
- When evidence fails in real life: Rethinking sports injury rehabilitation and prevention.Brazilian journal of physical therapy · 2026Review
- Machine Learning Model Development and Evaluation for Non-contact Lower Limb Injury Risk Prediction in Elite Male Rugby Union: A Multi-team, Multi-season Analysis.Sports medicine - open · 2026Article
- Predicting movement intensity through anthro-fitness profiling and its association with match outcomes in youth badminton: a logistic regression-based machine approach.BMC sports science, medicine & rehabilitation · 2026Article
- Deep Learning for Sensor-Based Sport Performance and Health Monitoring: A Review of Wearable, Vision-Based, and Multimodal Sensing Approaches.Sensors (Basel, Switzerland) · 2026Review
- Application of Artificial Intelligence for Predicting Sports Injuries and Customizing Personalized Prevention Strategies: A Scoping Review.Bioengineering (Basel, Switzerland) · 2026Review
- Artificial Intelligence in Sports Medicine: A Decision-Centered Framework for the Future Sports Physician.Diagnostics (Basel, Switzerland) · 2026Review
- Predicting self-reported injury status among runners training for the New York City Marathon.PM & R : the journal of injury, function, and rehabilitation · 2026Observational
- Emergency Clinical Decision for Sports Injury Management: A Wearable Sensor-Driven Framework from Training to Rehabilitation.Biosensors · 2026Review
- Utilization of machine learning to identify lower extremity biomechanical predictors of rupture in a validated cadaveric model of ACL injury.Scientific reports · 2026Article
- An explainable dual-attention transformer for predicting the sociocultural impact of global sports events.Scientific reports · 2026Article
- Can We Predict Adductor Strain? A Predictive Analysis of a Major League Soccer (MLS) Cohort Spanning from 2019 to 2022.Journal of functional morphology and kinesiology · 2026Article
- Machine Learning in Adapted Physical Activity: Clinical Applications, Monitoring, and Implementation Pathways for Personalized Exercise in Chronic Conditions: A Narrative Review.Journal of functional morphology and kinesiology · 2026Review
- [Progress and application of machine learning in sports injury research].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study reviewed the current state of machine learning (ML) research for the prediction of sports-related injuries. It aimed to chart the various approaches used and assess their efficacy, considering factors such as data heterogeneity, model specificity and contextual factors when developing predictive models.
designScoping review. DATA SOURCES: PubMed, EMBASE, SportDiscus and IEEEXplore.
resultsIn total, 1241 studies were identified, 58 full texts were screened, and 38 relevant studies were reviewed and charted. Football (soccer) was the most commonly investigated sport. Area under the curve (AUC) was the most common means of model evaluation; it was reported in 71% of studies. In 60% of studies, tree-based solutions provided the highest statistical predictive performance. Random Forest and Extreme Gradient Boosting (XGBoost) were found to provide the highest performance for injury risk prediction. Logistic regression outperformed ML methods in 4 out of 12 studies. Three studies reported model performance of AUC>0.9, yet the clinical relevance is questionable.
conclusionsA variety of different ML models have been applied to the prediction of sports-related injuries. While several studies report strong predictive performance, their clinical utility can be limited, with wide prediction windows or broad definitions of injury. The efficacy of ML is hampered by small datasets and numerous methodological heterogeneities (cohort sizes, definition of injury and dependent variables), which were common across the reviewed studies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.