{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T13:32:46Z","timestamp":1791639166277,"version":"4.3.4"},"reference-count":165,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T00:00:00Z","timestamp":1760313600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Italian Ministry of University and Research","award":["PRIN 2020L45ZWA_005"],"award-info":[{"award-number":["PRIN 2020L45ZWA_005"]}]},{"name":"Italian Ministry of University and Research","award":["PRIN-PNRR 2022: P2022NRRB8"],"award-info":[{"award-number":["PRIN-PNRR 2022: P2022NRRB8"]}]},{"name":"Italian Ministry of University and Research","award":["PRIN 2020L45ZW4_005"],"award-info":[{"award-number":["PRIN 2020L45ZW4_005"]}]},{"name":"PNRR\u2014the National Center for Gene Therapy and Drugs based on RNA Technology","award":["CN00000041"],"award-info":[{"award-number":["CN00000041"]}]},{"DOI":"10.13039\/501100003196","name":"Italian Ministry of Health","doi-asserted-by":"crossref","award":["PSC SALUTE 2014\u20132020-POS4 \u201cCal-Hub-Ria\u201d (T4-AN-09)"],"award-info":[{"award-number":["PSC SALUTE 2014\u20132020-POS4 \u201cCal-Hub-Ria\u201d (T4-AN-09)"]}],"id":[{"id":"10.13039\/501100003196","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003196","name":"Italian Ministry of Health","doi-asserted-by":"crossref","award":["PNRR MAD-2022\u201312376814"],"award-info":[{"award-number":["PNRR MAD-2022\u201312376814"]}],"id":[{"id":"10.13039\/501100003196","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Artificial intelligence (AI) is transforming cardiac electrophysiology across the entire care pathway, from arrhythmia detection on 12-lead electrocardiograms (ECGs) and wearables to the guidance of catheter ablation procedures, through to outcome prediction and therapeutic personalization. End-to-end deep learning (DL) models have achieved cardiologist-level performance in rhythm classification and prognostic estimation on standard ECGs, with a reported arrhythmia classification accuracy of \u226595% and an atrial fibrillation detection sensitivity\/specificity of \u226596%. The application of AI to wearable devices enables population-scale screening and digital triage pathways. In the electrophysiology (EP) laboratory, AI standardizes the interpretation of intracardiac electrograms (EGMs) and supports target selection, and machine learning (ML)-guided strategies have improved ablation outcomes. In patients with cardiac implantable electronic devices (CIEDs), remote monitoring feeds multiparametric models capable of anticipating heart-failure decompensation and arrhythmic risk. This review outlines the principal modeling paradigms of supervised learning (regression models, support vector machines, neural networks, and random forests) and unsupervised learning (clustering, dimensionality reduction, association rule learning) and examines emerging technologies in electrophysiology (digital twins, physics-informed neural networks, DL for imaging, graph neural networks, and on-device AI). However, major challenges remain for clinical translation, including an external validation rate below 30% and workflow integration below 20%, which represent core obstacles to real-world adoption. A joint clinical engineering roadmap is essential to translate prototypes into reliable, bedside tools.<\/jats:p>","DOI":"10.3390\/bioengineering12101102","type":"journal-article","created":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:13:27Z","timestamp":1760361207000},"page":"1102","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Artificial Intelligence in Cardiac Electrophysiology: A Clinically Oriented Review with Engineering Primers"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2800-0200","authenticated-orcid":false,"given":"Giovanni","family":"Canino","sequence":"first","affiliation":[{"name":"Department of Experimental and Clinical Medicine, Magna Graecia University, 88100 Catanzaro, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9963-6402","authenticated-orcid":false,"given":"Assunta","family":"Di Costanzo","sequence":"additional","affiliation":[{"name":"Department of Medical and Surgical Sciences, Magna Graecia University, 88100 Catanzaro, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nadia","family":"Salerno","sequence":"additional","affiliation":[{"name":"Department of Experimental and Clinical Medicine, Magna Graecia University, 88100 Catanzaro, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4774-0888","authenticated-orcid":false,"given":"Isabella","family":"Leo","sequence":"additional","affiliation":[{"name":"Department of Experimental and Clinical Medicine, Magna Graecia University, 88100 Catanzaro, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1502-2387","authenticated-orcid":false,"given":"Mario","family":"Cannataro","sequence":"additional","affiliation":[{"name":"Department of Medical and Surgical Sciences, Magna Graecia University, 88100 Catanzaro, Italy"},{"name":"Data Analytics Research Center, Magna Graecia University, 88100 Catanzaro, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5542-2997","authenticated-orcid":false,"given":"Pietro Hiram","family":"Guzzi","sequence":"additional","affiliation":[{"name":"Department of Medical and Surgical Sciences, Magna Graecia University, 88100 Catanzaro, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pierangelo","family":"Veltri","sequence":"additional","affiliation":[{"name":"Department of Medical and Surgical Sciences, Magna Graecia University, 88100 Catanzaro, Italy"},{"name":"DIMES (Dipartimento di Ingegneria Informatica, Modellistica, Elettronica e Sistemistica), University of Calabria, 87036 Rende, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sabato","family":"Sorrentino","sequence":"additional","affiliation":[{"name":"Department of Medical and Surgical Sciences, Magna Graecia University, 88100 Catanzaro, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5388-942X","authenticated-orcid":false,"given":"Salvatore","family":"De Rosa","sequence":"additional","affiliation":[{"name":"Department of Medical and Surgical Sciences, Magna Graecia University, 88100 Catanzaro, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4915-5084","authenticated-orcid":false,"given":"Daniele","family":"Torella","sequence":"additional","affiliation":[{"name":"Department of Experimental and Clinical Medicine, Magna Graecia University, 88100 Catanzaro, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bartusik-Aebisher, D., Rog\u00f3\u017c, K., and Aebisher, D. 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