Article: How AI IS Looking Out for Athletes' Health and Safety

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No sector of society appears immune to AI's influence, including the intersection of sports and player health. Pro athletes, franchise owners, and the myriad supporting businesses that provide everything from training and nutrition to medical care must carefully understand the changing environment ushered in by AI. Most professional athletes, including football and baseball players, negotiate the minimum number of games they will play per season as part of the collective bargaining process, but what happens if an AI assistant determines that a player will likely get injured in an upcoming game based on the AI's analysis of the player's current health? What if the player or the coaches disregard the AI recommendation and an injury occurs as predicted by the AI health bot?

Such a scenario may sound farfetched, but computing and sports medicine advances have made such a reality. AI excels at classifying and predicting outcomes when provided with large amounts of data, and professional sports generate vast amounts of data from each player while training, playing a game, and even at rest. Such biometric information from wearable devices that collect heart rate, sleep patterns, skin conductance for stress estimation, and more build a health profile in real-time of an athlete's health. Such information, combined with laboratory results such as metabolic profiles such as heart, liver, and kidney function, add dimensions to an athlete's health and strength capabilities. Additionally, AI excels at analyzing an athlete's biomechanics, from how they run and cut from side to side, swing a bat, kick a ball, or tackle another player. Such information helps an AI model suggest improvements to make a player more effective, as well as give hints as to whether a player may over-tax a joint or tendon that may lead to an injury shortly.

Such injury predictions rely on AI noticing delicate patterns in player performance that are not necessarily visible to a coach or trainer. One such system, BeOne Sports, developed by researcher athletes at Rice University, uses pose analytics to identify in real-time on the field how a player's motions differ from top athletes in their sport. (beonesports.com) The company claims to have accumulated over 100 million data points from over 150 top athletes embedded in their AI that can help players and coaches analyze players for improved performance from running, kicking, throwing, and catching on the training field with a smartphone or tablet. Such information, combined with medical history and injury history, helps the AI bot predict if an injury looks imminent. For example, if a baseball pitcher has suffered an elbow injury in the past, careful examination of the player's throwing mechanics could identify specific changes in pose and release of the ball that indicate compensation for an imminent injury. Such a warning could help a trainer or coach alter the pitcher's training regimen to stave off an injury.

The advances in AI technology for analyzing athletic performance have reached across the sporting world, from football and soccer to golf and tennis to every athletic endeavor. Professional teams spend millions to optimize their athletes' performance and help to prevent injury. New tools use complex algorithms to combine performance biometrics, health history, and real-time video analysis to assess the health and capabilities of athletes. As the predictive power of AI improves in injury risk assessment, new dilemmas will arise for coaches, athletes, and the role of risk in athletics. Take, for example, a professional contract that requires a minimum of playing time for an athlete to fulfill their contract to get paid for their services. However, an AI assistant may assert that the athlete has a high risk of sustaining a debilitating injury to the knee or a concussion. Should the coach insist, the player plays despite the risk. What if the player and her agent insist on playing time to fulfill the contract? New ethical and legal concerns will arise with superior predictive analytics infiltrating sports at all levels. The AI may be correct, but contracts may change to include the opinion of an AI bot in determining if a player should not play but still get paid.

 Dr. Smith’s career in scientific and information research spans the areas of bioinformatics, artificial intelligence, toxicology, and chemistry. He has published a number of peer-reviewed scientific papers. He has worked over the past seventeen years developing advanced analytics, machine learning, and knowledge management tools to enable research and support high-level decision making. Tim completed his Ph.D. in Toxicology at Cornell University and a Bachelor of Science in chemistry from the University of Washington.

You can buy his book on Amazon in paperback and in kindle format here.