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

Area of Science:

• Sports Medicine
• Data Science
• Biotechnology

Background:

• Sports injuries pose significant risks to athletes' health and performance.
• Traditional injury prevention methods often lack personalization and real-time adaptability.
• Advancements in artificial intelligence (AI) offer novel approaches to address these limitations.

Purpose of the Study:

• To comprehensively review the role of AI in predicting and preventing sports injuries.
• To explore the application of machine learning (ML) and deep learning (DL) techniques in this domain.
• To identify challenges and future directions for AI in sports injury management.

Main Methods:

• A systematic literature review was conducted across major scientific databases (PubMed, Google Scholar, Science Direct, Web of Science).
• Studies published between 2014 and 2024 using keywords like 'artificial intelligence', 'machine learning', 'sports injury', and 'risk prediction' were included.
• Analysis focused on AI techniques such as random forests (RFs), convolutional neural networks (CNNs), and artificial neural networks (ANNs).

Main Results:

• AI models demonstrate significant potential in analyzing complex datasets to identify injury patterns and predict risks.
• Personalized injury prevention strategies can be developed by tailoring AI models to individual athlete profiles and real-time data.
• AI applications show promise in both individual and team sports, though team sports present unique data integration challenges.

Conclusions:

• AI is transforming sports injury management from reactive to proactive, enhancing athlete safety and performance.
• Addressing data quality, ethical considerations, and transparency is crucial for the effective implementation of AI in sports.
• Continued evolution of AI promises further advancements in optimizing athlete health and reducing medical errors.