Artificial Intelligence in Cardiac Electrophysiology: A Comprehensive Review

first_pagesettingsOrder Article Reprints Open AccessReview Artificial Intelligence in Cardiac Electrophysiology: A Comprehensive Review by Pietro Cipollone 1,Nicola Pierucci 1,*ORCID,Andrea Matteucci 2ORCID,Marta Palombi 1ORCID,Domenico Laviola 1ORCID,Raffaele Bruti 1,Sara Vinciullo 1,Marco Bernardi 3ORCID,Luigi Spadafora 3,Angelica Cersosimo 4ORCID,Sara Trivigno 1,Tommaso Recchioni 1ORCID,Agostino Piro 1ORCID,Cristina Chimenti 1,Claudio Pandozi 2,Carmine Dario Vizza 1ORCID,Carlo Lavalle 1ORCID andMarco Valerio Mariani 1 1 Department of Cardiovascular, Respiratory, Nephrological, Aenesthesiological and Geriatric Sciences “Sapienza”, University of Rome, 00161 Rome, Italy 2 Clinical and Rehabilitation Cardiology Division, San Filippo Neri Hospital, 00135 Rome, Italy 3 Department of Medical-Surgical Sciences and Biotechnologies, Sapienza University of Rome, 00185 Latina, Italy 4 Division of Cardiology and Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, 25121 Brescia, Italy * Author to whom correspondence should be addressed. J. Pers. Med. 2025, 15(11), 532; https://doi.org/10.3390/jpm15110532 Submission received: 31 July 2025 / Revised: 28 September 2025 / Accepted: 1 October 2025 / Published: 3 November 2025 (This article belongs to the Special Issue Atrial Fibrillation: Toward Personalized Medicine) Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract Background: Artificial Intelligence (AI) is a transformative innovation designed to enable machines to perform tasks typically requiring human intelligence. Among various medical fields, cardiology—and particularly electrophysiology—has seen rapid integration of AI technologies. The ability of AI to analyze large and complex datasets is reshaping diagnostic and therapeutic approaches. Objectives: This review aims to provide a comprehensive overview of AI models and their applications in cardiac electrophysiology. The focus is on understanding how AI contributes to clinical practice through ECG interpretation, arrhythmia detection, atrial mapping, and catheter ablation, while also exploring its limitations and future potential. Methods: The review discusses various AI approaches, including Machine Learning (ML) and Deep Learning (DL), and highlights relevant literature illustrating their implementation in electrophysiological settings. Key clinical applications are examined thematically, with a narrative synthesis of current capabilities, technologies, and outcomes. Results: AI-based tools have demonstrated effectiveness in identifying supraventricular arrhythmias like atrial fibrillation (AF) and atrial flutter (AFL), as well as complex conditions such as ventricular tachycardias (VTs) and long QT syndrome (LQTS). In procedural contexts, AI enhances electro-anatomical mapping, reduces operative time, and supports tailored post-ablation management. Discussion: While AI offers clear advantages in diagnostic accuracy and procedural efficiency, challenges remain regarding data security, ethical transparency, and clinical adoption. Addressing these limitations will be crucial for integrating AI into routine electrophysiology and maximizing its potential in future cardiology practice.