Sudden cardiac death remains the leading medical cause of mortality among young athletes. Emerging research indicates that explainable artificial intelligence, paired with advanced digital heart models, may identify high-risk individuals before catastrophic events occur.
Public scrutiny of athlete cardiac safety intensified recently after Danish footballer Christian Eriksen suffered a collapse during a match, requiring intervention from his implantable cardioverter defibrillator. This incident underscores the critical need for clinicians to detect dangerous arrhythmias proactively.
A systematic review analyzing data from 2013 to 2025 examined 84 studies focusing on sports-related sudden cardiac death, explainable AI, and cardiac modeling. The incidence rate ranges from 0.1 to 0.6 cases per 100,000 participants annually. While statistically rare, the unpredictability and severity of these events demand improved screening mechanisms.
The review highlights Gradient-weighted Class Activation Mapping as a predominant explainable AI technique. Unlike black-box algorithms, this method allows clinicians to visualize which features drive predictions, fostering trust in AI-assisted diagnostics. When integrated with cardiac electrophysiological models that simulate electrical activity at cellular and tissue levels, these tools offer personalized risk assessments.
Experts argue that standardizing definitions and integrating epidemiological data with AI and modeling frameworks are essential next steps. This convergence promises to enhance early detection and strengthen preventive strategies for vulnerable athletes.