Artificial Intelligence (AI) has become one of the most transformative forces in modern sports. Its influence spans from diagnosing injuries and improving player performance to officiating matches and shaping the economics of athletic training. Yet, while AI offers enormous promise, it also raises questions about cost, access, and fairness.
One of the earliest documented applications of AI in sports injury diagnosis came in 1997, when Zelič, Kononenko, Lavrač, and Vuga at the University of Ljubljana’s sports medicine clinic published a landmark study. Their work, Induction of Decision Trees and Bayesian Classification Applied to Diagnosis of Sport Injuries, demonstrated that AI could support physicians in diagnosing injuries with limited data. The researchers used 118 clinical records from athletes in athletics (track) and handball (soccer), organizing them into 30 diagnostic classes like Knee ligaments injuries, muscle strains and sprains, stress fractures, contusions, soft tissue trauma and more. Conventional statistical methods struggled with the small dataset, so the team tested two machine learning approaches named Decision Trees (ASSISTANT family) and Bayesian Classifiers.
Decision trees work by asking a series of yes or no questions that gradually narrow down a diagnosis. The decision tree learns patterns that mirrors a doctor’s clinical reasoning and then extracts them directly from data. For example, If there’s swelling, and instability then likely it’s a ligament injury. Whereas, if there’s swelling and no instability then likely it’s a meniscus injury. This approach helped reveal hidden relationships between symptoms and injuries, even in small datasets. Bayesian Classifiers probabilistic models that estimated the likelihood of injuries based on observed symptoms, supported by fuzzy discretization to account for small datasets. The Bayesian classifier used probabilities instead of rules. For example, If there’s swelling, instability and pain on rotation there is an 82% probability of a ligament injury and 15% of a meniscus tear. Bayesian models are powerful because they can still function with missing or uncertain data which is common to today’s medical records. These studies are important because it proves that machine learning could assist in diagnosing sports injuries even with small datasets decades before big data and deep learning. Their work influenced later developments in AI-based injury risk prediction like Olympics, FIFA and more.
Since then, AI has advanced rapidly, with wearables tracking athlete workloads, predictive models helping prevent injuries, and deep learning tools analyzing motion and reading scans with near-human accuracy. The financial side, however, highlights a drawback. High-end devices like anti-gravity treadmills, which can cost $35,000 to $75,000. The University of Alabama Crimson Tide football team has one in their facility, mainly used to help athletes recover from lower-body injuries while reducing impact on joints. Common injuries behind its use include, ACL tears or reconstructions, and ankle sprains and fractures. By allowing athletes to run at a fraction of their body weight, they can maintain cardiovascular fitness while recovering from an injury. Robotic gait systems costing six figures, place AI-driven technology out of reach for many smaller teams and clinics.
On the performance side, AI enhances training by breaking down biomechanics, predicting recovery needs, and personalizing nutrition. Coaches also benefit from advanced video analysis, opponent scouting, and real-time tactical insights during games. Yet, heavy dependence on dashboards and data risks undervaluing human judgment and instinct, which remain central to coaching. For example, Big League AI app subscription based, helps in baseball/softball: analyze swings via phone video, give feedback on mechanics, and velocity. PlaySight SmartCourt helps in tennis, basket $10,000 Cameras + software installation system that tracks strokes/plays, offers multi-angle video replays, live streaming, analytics. Perhaps the most visible use of AI is in officiating. VAR in soccer and Hawk-Eye in tennis and cricket provide accurate calls and reduce human bias, but they also introduce delays and spark debate when fans feel that “machines” are deciding outcomes.
Overall, AI offers undeniable benefits like safer athletes, smarter strategies, and fairer competition. At the same time, challenges such as high costs, unequal access, and over-reliance on data show that the technology must be balanced with the human spirit of sport.
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