ReviewDiagnostics (Basel, Switzerland)2024
Diagnostic Applications of AI in Sports: A Comprehensive Review of Injury Risk Prediction Methods.
Review in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled 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
- Artificial intelligence and wearable sensors in sports injury risk prediction: current status and future perspectives.Annals of medicine · 2026Review
- Article
- 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
- Wearable Biosensing and Machine Learning for Data-Driven Training and Coaching Support.Biosensors · 2026Review
- Artificial intelligence in postural management: a critical review of detection, correction, and clinical applicability.Journal of orthopaedic surgery and research · 2026Review
- Development and validation of a machine learning model for non-contact injury prediction based on lower limb strength asymmetry in professional football.Scientific reports · 2026Article
- Review
- Key biomechanical features of jump-landing under cognitive dual-task conditions: an XGBoost-SHAP-based explainable machine learning analysis.Frontiers in bioengineering and biotechnology · 2026Article
- Explainable temporal deep learning for athlete-independent classification of injury-labeled days in competitive distance runners: a methodological benchmark using seven-day training-load histories.Frontiers in public health · 2026Article
- A multimodal deep learning-based model for posture asymmetry recognition and sports injury risk prediction in adolescent table tennis athletes.Frontiers in physiology · 2026Article
- Digital twin for Taekwondo athletes: integrating sports nutrition and psychological readiness using artificial intelligence.Frontiers in public health · 2026Review
- Epidemiology, injury mechanisms, and prevention strategies in Ultimate Frisbee.Frontiers in sports and active living · 2026Review
- A Novel Fabric Strain Sensor Array with Hybrid Deep Learning for Accurate Knee Movement Recognition.Micromachines · 2025Article
- Sports injury risk prediction based on temporal graph encoding and graph neural networks: A cross-sport transfer learning framework.Scientific reports · 2025Article
- Intelligent optimization of track and field teaching using machine learning and wearable sensors.Scientific reports · 2025Article
- Strategies for preventing anterior cruciate ligament injuries in athletes: Insights from a scoping review.Journal of orthopaedics · 2025Review
- A narrative review of deep learning applications in sports performance analysis: current practices, challenges, and future directions.BMC sports science, medicine & rehabilitation · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This review provides a comprehensive analysis of the transformative role of artificial intelligence (AI) in predicting and preventing sports injuries across various disciplines. By exploring the application of machine learning (ML) and deep learning (DL) techniques, such as random forests (RFs), convolutional neural networks (CNNs), and artificial neural networks (ANNs), this review highlights AI's ability to analyze complex datasets, detect patterns, and generate predictive insights that enhance injury prevention strategies. AI models improve the accuracy and reliability of injury risk assessments by tailoring prevention strategies to individual athlete profiles and processing real-time data. A literature review was conducted through searches in PubMed, Google Scholar, Science Direct, and Web of Science, focusing on studies from 2014 to 2024 and using keywords such as 'artificial intelligence', 'machine learning', 'sports injury', and 'risk prediction'. While AI's predictive power supports both team and individual sports, its effectiveness varies based on the unique data requirements and injury risks of each, with team sports presenting additional complexity in data integration and injury tracking across multiple players. This review also addresses critical issues such as data quality, ethical concerns, privacy, and the need for transparency in AI applications. By shifting the focus from reactive to proactive injury management, AI technologies contribute to enhanced athlete safety, optimized performance, and reduced human error in medical decisions. As AI continues to evolve, its potential to revolutionize sports injury prediction and prevention promises further advancements in athlete health and performance while addressing current challenges.
Indexed as
Identifiers
What OpenQuestion holds
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.