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For decades, cardiovascular medicine has been a discipline of ‘averages’ and ‘thresholds’—relying on population-based risk scores and human interpretation of single clinical results. As we navigate an era defined by data proliferation, the integration of artificial intelligence (AI) has transitioned from a futuristic concept to a clinical imperative.1 2 While the potential of AI is widely acknowledged, a ‘translational gap’ has persisted between algorithmic development and bedside application, especially in the field of cardiovascular diseases.3 This newest topic collection ‘AI in the Diagnosis and Management of Heart Disease’ in Open Heart serves as a bridge across this divide, showcasing studies that enhance diagnostic precision, risk stratification and therapeutic management.