Atrial Fibrillation: Toward Personalized Medicine

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Mechanisms of Diseases".

Deadline for manuscript submissions: 25 January 2027 | Viewed by 2867

Special Issue Editor


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Guest Editor
Texas Cardiac Arrhythmia Institute, St David's Medical Center, Austin, TX, USA
Interests: cardiac arrhythmias, atrial fibrillation; heart failure; arrhythmia; sudden cardiac death; catheter ablation
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Special Issue Information

Dear Colleagues,

Atrial fibrillation (AF) is a multifactorial condition characterized by marked heterogeneity in clinical presentation, risk profiles, and treatment responses. Recent advances in genetics have identified specific variants that predispose young individuals to AF, highlighting the importance of early detection and personalized therapeutic strategies. Simultaneously, the development of next-generation electroanatomical mapping systems is transforming our ability to define patient-specific atrial substrates, enabling more precise and effective ablation therapies. One of the most significant recent breakthroughs is the clinical introduction of pulsed field ablation—a non-thermal energy source that offers myocardial selectivity, reduced collateral damage, and enhanced procedural safety. With the increasing availability of genomic, imaging, and computational tools, the field is moving toward a more nuanced and individualized approach to rhythm management.

This Special Issue of the Journal of Personalized Medicine will explore the latest advancements in personalized strategies for AF, from genetic insights and technological innovations to their integration into clinical workflows. Contributions from both basic science and clinical practice are welcome to help shape the future of precision electrophysiology.

Dr. Vincenzo Mirco La Fazia
Guest Editor

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Keywords

  • atrial fibrillation
  • personalized medicine
  • genetic predisposition
  • electroanatomical mapping
  • pulsed field ablation
  • genomics
  • substrate characterization
  • tailored ablation strategies

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Published Papers (1 paper)

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Review

18 pages, 2601 KB  
Review
Artificial Intelligence in Cardiac Electrophysiology: A Comprehensive Review
by Pietro Cipollone, Nicola Pierucci, Andrea Matteucci, Marta Palombi, Domenico Laviola, Raffaele Bruti, Sara Vinciullo, Marco Bernardi, Luigi Spadafora, Angelica Cersosimo, Sara Trivigno, Tommaso Recchioni, Agostino Piro, Cristina Chimenti, Claudio Pandozi, Carmine Dario Vizza, Carlo Lavalle and Marco Valerio Mariani
J. Pers. Med. 2025, 15(11), 532; https://doi.org/10.3390/jpm15110532 - 3 Nov 2025
Cited by 6 | Viewed by 2484
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Atrial Fibrillation: Toward Personalized Medicine)
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