Transforming Sports Medicine with Deep Learning and Generative AI: Personalized Rehabilitation Protocols and Injury Prevention Strategies for Professional Athletes

Abstract

Personalized rehabilitation protocols and the development of effective injury prevention strategies are particularly important to professional athletes because of the nature of their lifestyle, and their adherence to such regimes often dictates their performance and career length. In practice, however, identifying and coping with subtle differences among athletes has proved to be a challenge in the field of sports medicine. A suite of modern technologies, deep learning and generative artificial intelligence in particular, are currently revolutionizing many aspects of our lives. Here, we discuss how these technologies are similarly transforming the field of sports medicine and allowing us to surmount many of the constraints that have proven to be limiting factors in the past. By using the right tool for the right job, several complex problems seen in professional athletes can be circumvented, providing us with specific examples to illustrate the point. These technologies can allow for mass individualization rather than mass personalization, which is driving change in professional sports, as well as affordable mass personalization in populations outside the realm of professional sports.

Rehabilitation protocols for athletes have, until now, mostly been based on clinical experiences rather than evidence-based models. The lack of objective measures in rehabilitation protocols hinders a quantifiable and well-monitored recovery period. Furthermore, baseline testing involves subjective assessments to gauge an athlete’s current level of fitness, without factoring in important biomechanical variances. A follow-up study focusing on assessors’ interpretations of individual strategies, effectiveness, and changes in conditioning or rehabilitation protocols did not provide useful findings. The testing also relied on a manual summation of each biomechanical feature used to prescribe the rehabilitation protocol, which can lead not only to human bias but also to a substantial delay in the diagnosis, increasing recovery times.