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Review
. 2024 Nov 10;14(22):2516.
doi: 10.3390/diagnostics14222516.

Diagnostic Applications of AI in Sports: A Comprehensive Review of Injury Risk Prediction Methods

Carmina Liana Musat  1 , Claudiu Mereuta  2 , Aurel Nechita  1 , Dana Tutunaru  1 , Andreea Elena Voipan  3 , Daniel Voipan  3 , Elena Mereuta  4 , Tudor Vladimir Gurau  1 , Gabriela Gurău  1 , Luiza Camelia Nechita  1
Affiliations
Review

Diagnostic Applications of AI in Sports: A Comprehensive Review of Injury Risk Prediction Methods

Carmina Liana Musat et al. Diagnostics (Basel). .

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.

Keywords: artificial intelligence; individual sports; injury risk prediction; musculoskeletal injuries; prediction methods; sports medicine; team sports; technical review.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
The hierarchy of the AI technologies used in sports injury prediction.
Figure 2
Figure 2
Basic architecture of ML models used in sports injury prediction: (a) RF; (b) SVM; (c) KNN.
Figure 3
Figure 3
Basic architecture of DL models used in sports injury prediction: (a) CNN; (b) ANN; (c) RNN.
Figure 4
Figure 4
Key steps in data analysis for AI.
Figure 5
Figure 5
Systematic literature review process for the technical analysis of injury risk prediction methods that use AI.
See this image and copyright information in PMC

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