Machine Learning and Large Language Models in Athlete Cardiac Care: Asystematic Review of Applications and Challenges

Learning Objectives:

• Upon completion, participants will be able to understand the diverse applications of machine learning and large language models in athlete cardiac care, including their use in imaging interpretation, risk stratification, and fatigue assessment.
• Upon completion, participants will be able to evaluate the performance and limitations of ML/LLM models in diagnosing subclinical cardiac conditions and predicting outcomes in athletic populations, with attention to accuracy, sensitivity, and predictive value.
• Upon completion, participants will be able to identify methodological gaps and future directions for integrating AI-based tools into clinical sports cardiology practice, including the need for improved generalizability, larger datasets, and model interpretability.