Background: Atrial fibrillation remains the most common sustained cardiac arrhythmia, with catheter ablation increasingly preferred over antiarrhythmic drugs in selected patients. Direct comparative effectiveness data from real-world populations remain sparse... This propensity-matched analysis draws on a multicenter administrative claims dataset to compare rates of AF recurrence, hospitalizations, and serious adverse events between patients receiving catheter ablation and those treated with antiarrhythmic drug therapy over a two-year follow-up period. Subgroup analyses explore outcomes by age, AF duration, and left atrial size.

Background: Machine learning algorithms applied to electrocardiographic, imaging, and electronic health record data have demonstrated striking discriminative performance in cardiovascular risk stratification. Yet the translation of algorithmic performance to clinical utility demands a more nuanced framework... This editorial examines the gap between model discrimination metrics and patient-level benefit, highlighting issues of dataset bias, lack of prospective validation, and regulatory pathways. We propose a framework for evaluating AI tools in cardiology that centers on clinical workflow integration, interpretability, and equity across patient populations.