The economic burden of sports injuries is staggering. In 2019, the total economic toll of injuries across the United States reached $4.2 trillion, a figure that captures both immediate medical costs and long-term consequences. Of that amount, approximately $327 billion was explicitly allocated to medical care, according to the Centers for Disease Control and Prevention. Youth sports injuries alone have profoundly affected individuals, families, and the broader healthcare system, and an examination by the National Institutes of Health estimates that the United States spends between $0.8 billion and $5.2 billion annually on injuries related to sport specialization. These numbers illustrate the variability in costs depending on the specific sports and types of injuries incurred, and they underscore the need for targeted prevention measures implemented through collaboration among clinicians, engineers, and athletic organizations.

At the core of this transformation lies biomechanics and motion analysis. Understanding the mechanics of human movement plays a crucial role in sports medicine, and innovations in motion capture technology, including high-speed cameras, inertial measurement units, and force plates, now enable detailed analysis of an athlete’s biomechanics. These systems help identify improper movement patterns that can lead to injuries and aid in developing effective rehabilitation protocols. Wearable technology has transformed athlete monitoring in parallel. Smartwatches, heart rate monitors, and GPS tracking systems collect real-time data on health metrics, performance, and fatigue levels, allowing coaches and medical staff to tailor training regimens and closely monitor athletes’ physical conditions. Diagnostic imaging advances, including MRI, CT scans, and ultrasound, enable precise diagnosis and evaluation of sports injuries such as fractures, ligament tears, muscle strains, and joint problems, contributing to better decision-making regarding treatment options and a better understanding of the extent of injuries.

Most sports injuries result from overuse and inadequate recovery and rest. Overuse can lead to the wear and tear of muscles, tendons, and joints, yet athletes and their coaches often remain unaware that the probability of an injury is rising because there have been no effective methods to measure fatigue. Teams across various sports are now starting to integrate technologies such as smartwatches, wireless integrated circuits, and motion-capture cameras to track the vitals of their athletes, along with using a gyroscope, accelerometer, and blood-oxygen sensor to capture additional parameters. Rossi and colleagues, whose work on injury forecasting in soccer is cited in the article, collect data from players using a tight top under their jerseys outfitted with GPS, an accelerometer, a gyroscope, and a digital compass. Technologies like these enable coaches, trainers, and athletes to more accurately forecast potential injuries by monitoring an athlete’s biomechanical and metabolic status during games or practices.

The application of this technology has expanded beyond soccer into football and baseball. Professional football teams increasingly use force plates to measure ground reaction forces and predict injury risk, and research using the Sparta Science force plate has shown a notable decline in musculoskeletal health following COVID-19 lockdown measures. Applying markerless motion capture is an additional AI-driven approach for analyzing sports performance and evaluating injury risk, particularly for baseball pitchers engaged in repetitive overhead throwing, where the technology has demonstrated its efficacy in assessing injury risk. Nonetheless, ongoing discussions persist regarding how these technologies can extrapolate injury risk data to other athlete populations. For injury protection to deliver its benefits, data collection must be wirelessly filtered, structured, and analyzed using data mining, AI, and machine learning algorithms, with feedback from clinical professionals, to accurately assess athletes’ health status and provide intelligent recommendations for personalized assessment and training methods.

The effectiveness of any predictive model depends on a multitude of factors. At the core of its efficacy lie the quality and quantity of the data used for both training and evaluation. When the data is of poor quality, due to errors, inconsistencies, or biases, or the dataset is insufficient in volume, the result can be a pronounced deviation in the model’s accuracy when predicting risks. Moreover, the selection of appropriate features, coupled with meticulous preprocessing and feature engineering, plays a pivotal role in shaping the model’s success. The model’s architecture and parameter configurations are equally crucial determinants of its learning capacity and expressiveness, and opting for an architecture and fine-tuning parameter settings that align with the task’s intricacies can substantially enhance the model’s overall effectiveness. Despite significant advancements in data collection for athletes over the past decade, systematic methods and platforms remain inadequate for fully leveraging that data, presenting an opportunity to apply data science and analytics to unlock its value.

Augmented reality and virtual reality have emerged as powerful tools in both rehabilitation and surgical planning. AR and VR can enhance rehabilitation and injury recovery by creating immersive environments that help athletes regain strength, balance, and coordination while performing targeted exercises. They can capture an athlete’s movements and provide real-time visual feedback, allowing for analysis and correction of movements, and VR can create realistic sports scenarios and training environments that allow athletes to practice their skills in a controlled setting. Both technologies can simulate high-pressure situations and enhance mental skills such as focus, concentration, and visualization, which are critical for sports performance. In the operating room, orthopedic departments worldwide are implementing mixed reality technology and 3D printing to enhance patient outcomes. Virtual reality can provide residents and fellows with supplemental training in procedures, helping close the gap in experience gained during residency, which is highly dependent on the geographic location of training. Augmented reality can go further, combining images and planning to provide individualized patient anatomy and incision locations without requiring ionizing radiation on the patient, while mixed reality offers holographic overlays and video conferencing for collaboration with colleagues or educational experiences for trainees.

The authors also outline a visionary path forward built on data integration. Finite element analysis has been used to develop joint models from CT images converted into CAD files, allowing biomechanical experiments to determine load distribution and joint deformation under specific conditions. However, a potential pitfall of these experiments is that they do not account for the personalized nature of each athlete, failing to consider the dynamics and contributions of the musculoskeletal structures around the joints. Athletes with previous injuries may be compensating differently under joint stress, and each athlete may load their joints differently during movements specific to their sport. Several investigators are pursuing a combination of finite element analysis and multibody dynamics, which could pave the way for better modeling and simulation of specific exercises. Looking ahead, the authors envision collecting and seamlessly integrating kinematic data from wearable devices and markerless motion capture into these models using patient-specific scans, replicating an athlete’s movements and the resulting joint stresses, with data stratified by sport, position, and prior injuries.

The implications for surgical planning and rehabilitation are profound. Consider a football player who recently suffered an ACL tear. This player has existing kinematic data capturing their movements during practice and on-field activities, as well as real-time data recorded at the time of injury. If a surgeon could access this data and utilize suitable software to visualize the player’s movements, it would provide a more comprehensive understanding of the injury mechanism. Combining CT images of the player’s knee taken before and after the injury with 3D printing technology enables the creation of an almost perfect replica of their ACL, and the amalgamation of kinematic and CT image data accumulated across numerous athlete injuries holds the potential to develop AI systems that assist with surgical planning and identify the most effective approach to ACL repair. On the rehabilitation side, load management combines objective measurements, such as shots taken or pitches thrown, with subjective factors like the athlete’s rate of perceived exertion, and a systematic review has found moderate evidence linking an athlete’s training load during rehabilitation to injury. Integrating additional kinematic data from accelerometers and motion-tracking devices, along with electromagnetic sensors already used to monitor limb kinematics during post-surgery rehabilitation, could provide quantitative evaluation of reinjury risk and objective measures for approval of return to play.

Significant challenges remain before this vision becomes reality. The application of technology in sports medicine lags behind advancements in non-medical fields, particularly in the development of AI systems, and there is still a lack of seamless integration of athlete kinematic data, hindering the development of more robust research and surgical planning tools. Current biosensors are still in development and cannot continuously monitor biological biomarkers effectively, requiring improvements in sensitivity, selectivity, and accuracy to prevent false alarms and missed health detections, with validation against gold-standard methods crucial. Privacy and security concerns arise with the collection and transmission of sensitive health data, prompting healthcare providers and manufacturers to implement stringent protections. Standardization begins with establishing common metrics for athlete performance and health, such as heart rate, movement patterns, and fatigue levels, that every organization can adopt regardless of the sport, and variability in technology adoption across organizations can create unequal access to innovative training and rehabilitation tools. As more data is collected, distilling it into actionable insights becomes increasingly complex, and coaches and trainers must be adept at interpreting this information and communicating it effectively to athletes. Yet if the connection between data collection and utilization is established, the authors conclude, the possibilities for advancing the field of sports medicine become limitless, fostering a future where proactive, informed approaches to athlete care optimize performance and safeguard health in an ever-demanding sporting environment.

Subject of Research: Technological applications in sports medicine for injury prevention, diagnosis, and rehabilitation

Article Title: Advancing sports medicine through technology: some applications and future visions

Article References: Amirouche, F., & Patel, I. (2025). Advancing sports medicine through technology: some applications and future visions. Frontiers in Musculoskeletal Disorders, 3, Article 1609729. https://doi.org/10.3389/fmscd.2025.1609729

Image Credits: AI Generated

DOI: 10.3389/fmscd.2025.1609729

Keywords: sports medicine, wearable devices, artificial intelligence, motion capture, virtual reality, augmented reality, injury prevention, rehabilitation, diagnostic imaging, machine learning, biomechanics, 3D printing