AI in Diagnosing and Treating Sports Injuries

Suriname defender Liam Van Gelderen goes up for a header against Mexico forward Cesar Huerta during the second half during a group stage match of the 2025 Gold Cup at AT&T Stadium. Mandatory Credit: Jerome Miron-Imagn Images

Artificial intelligence (AI) is transforming the landscape of modern medicine, and sports medicine is no exception. It offers unparalleled precision, speed, and accessibility in diagnosing and managing sports-related injuries. From acute trauma to chronic overuse syndromes, these injuries represent a significant challenge across all levels of athletic participation. Traditional diagnostic approaches rely heavily on clinical acumen and imaging interpretation, whereas treatment strategies often adhere to generalized protocols that may not fully account for individual variability. The integration of AI, particularly through machine learning and deep learning, introduces a paradigm shift, with the potential to augment and, in some domains, surpass traditional diagnostic methods by enhancing accuracy, enabling risk stratification, and facilitating personalized rehabilitation pathways.

“Artificial intelligence is poised to revolutionize the diagnosis, management, and prevention of sports injuries.”

Diagnostic Imaging

Medical image analysis is one of the most well-established applications of AI in sports medicine. Convolutional neural networks, a type of deep learning model, demonstrate proficiency in interpreting musculoskeletal imaging, including magnetic resonance imaging (MRI) and ultrasound. An AI model can detect anterior cruciate ligament tears on MRIs with diagnostic accuracy comparable to that of musculoskeletal radiologists⁽¹⁾. Similarly, it can identify rotator cuff tears and meniscal injuries with high sensitivity and specificity⁽²⁾. These systems can reduce interpretation time and serve as decision-support tools, particularly in resource-limited settings.

Predictive Modeling

Furthermore, AI enables predictive analytics by identifying patterns and risk factors that may predispose athletes to injuries. To forecast injury risk, machine learning algorithms can analyze large datasets encompassing biometric data, training loads, biomechanics, and previous injuries. For instance, Spanish researchers used machine learning to predict hamstring injuries in football players with greater accuracy than traditional statistical models⁽³⁾. Wearable technology integrated with AI systems can offer real-time monitoring, alerting athletes and coaches to biomechanical anomalies or excessive strain, thereby preventing overuse injuries⁽⁴⁾.

Real-Time Monitoring and Decision Support

Artificial intelligence-integrated wearables are revolutionizing real-time athlete monitoring by providing actionable insights into biomechanics and movement patterns. For example, the National Basketball Association (NBA) utilizes Second Spectrum, an AI platform that analyzes player movement and load in real-time, to aid in performance and injury risk evaluation. Combined with wearable sensors, this has enabled more personalized rehabilitation and recovery timelines⁽⁵⁾. For example, the Catapult Sports system utilizes inertial measurement units and AI algorithms to detect asymmetries in movement during rehabilitation, enabling clinicians to intervene early in cases of compensatory biomechanics⁽⁶⁾.

Personalized Rehabilitation and Recovery

Injury recovery is another area where AI holds promise. Rehabilitation programs traditionally follow a one-size-fits-all model, but AI can support personalized rehabilitation pathways by analyzing progress metrics and adapting protocols accordingly. AI-driven motion capture and assessment tools can monitor movement patterns and provide feedback without the need for elaborate laboratory equipment. Researchers from Hokkaido University in Japan assessed the validity of AI-driven gait analysis systems that utilize a single video camera to measure bilateral lower limb kinematics. The findings indicated that these systems achieved "excellent" reproducibility and acceptable accuracy, suggesting their potential as accessible alternatives to traditional motion capture systems for clinical gait analysis⁽⁷⁾. Additionally, researchers from the University of Pittsburgh in the USA have shown that natural language processing tools can extract relevant patient information from clinical notes and optimize rehabilitation strategies⁽⁸⁾.