AI and machine learning analyze large amounts of biometric data collected from athletes during training. By examining metrics such as distance run and intensity of exertion, AI can predict potential injuries. For instance, research has shown that using machine learning models can predict up to 80% of potential injuries in soccer players.

The NFL has utilized injury prediction data to adjust training schedules and manage player exertion. This approach, particularly focusing on 'load management,' has led to a reported 26% decrease in lower extremity injuries during the first two weeks of the season.

One major limitation is the 'black box' problem, where certain AI models provide predictions without clear explanations, making it difficult for coaches to act on the information. Additionally, the effectiveness of data-driven approaches varies by sport, with team sports benefiting more from aggregated data than individual sports, where unique athlete profiles complicate predictions.