A Review of the Integration of Artificial Intelligence in Cardiac Electrophysiology

Cardiac electrophysiology (EP) is inherently data-centric, spanning brief 12-lead electrocardiograms (ECGs), high-density electroanatomic maps, and continuous device-based monitoring. This data volume can strain provider workflows while creating an opportunity for artificial intelligence (AI). Machine learning (ML) and its deep learning subfield extract clinically actionable patterns from complex electrical signals. This narrative review summarizes contemporary AI applications across the major domains of EP. In arrhythmia detection, deep neural networks classify rhythms at a level comparable to cardiologists on internal test sets, identify occult atrial fibrillation (AF) from a normal sinus-rhythm ECG, and, through consumer wearables, extend screening to ambulatory populations. In catheter ablation, an AI algorithm that adjudicates intracardiac electrogram dispersion improved single-procedure freedom from AF in a randomized trial of persistent AF, and ML models help predict arrhythmia recurrence; we distinguish these from adjacent non-AI technologies, such as computed-tomography integration and three-dimensional mapping, that reduce fluoroscopy but are not themselves AI. In cardiac implantable electronic devices (CIEDs), AI-based filtering lowers false-positive alert burden, and multi-parametric algorithms provide earlier prediction of heart-failure decompensation. ML models may refine patient selection for cardiac resynchronization therapy (CRT) and, using late-gadolinium-enhancement cardiac magnetic resonance, may sharpen arrhythmic-risk and implantable cardioverter-defibrillator (ICD) decision-making. AI-enhanced ECG broadens the standard ECG into a low-cost screening tool for channelopathies, dyskalemias, and ventricular dysfunction. Important barriers remain, including limited external validation, incomplete explainability, and a scarcity of prospective outcome trials.