Authors - Khadidje OUSMANE KOSSI, Mandicou BA, Bachar Haggar SALIM, Simon Antoine SARR, Maboury DIAO, Alassane BAH Abstract - Heart disease in athletes remains a significant challenge in sports cardiology and an important public health concern, particularly among young competitive individuals at risk of sudden cardiac events. Although pre-participation screening programs are widely implemented, diagnostic uncertainty persists, especially in distinguishing physiological cardiac remodeling from pathological cardiomyopathy. This complexity results from the interaction of genetic predisposition, structural adaptation, electrophysiological variability, and cumulative training exposure. Using the PRISMA framework, this study presents a systematic review of research published between 2015 and 2025 to evaluate the application of artificial intelligence (AI) in the diagnosis and monitoring of cardiovascular diseases in athletes. The analysis reveals that most studies rely on unimodal, monocentric, and retrospective designs, often based on limited datasets and lacking external validation. Despite high reported performance metrics, performance degradation of 5–10% in external cohorts is frequently observed. Furthermore, explainability techniques are inconsistently applied, and real-world clinical integration remains limited. Only a small number of studies adopt multimodal approaches integrating electrophysiological, imaging, biological, and training-related data. These limitations restrict the clinical translation of AI models. Future research should prioritize multicenter, diverse, and explainable multimodal frameworks to support reliable cardiovascular risk stratification and return-to-play decision making.
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