Background: Artificial intelligence (AI) is poised to significantly impact the future of cardiology and electrophysiology, offering new tools to interpret complex datasets, improve diagnosis, optimize clinical workflows, and personalize therapy. ChatGPT-4o, a leading AI-based language model, exemplifies the transformative potential of AI in clinical research, medical education, and patient care. Aim and Methods: In this paper, we present an exploratory dialogue with ChatGPT to assess the role of AI in shaping the future of cardiology, with a particular focus on arrhythmia management and cardiac electrophysiology. Topics discussed include AI applications in ECG interpretation, arrhythmia detection, procedural guidance during ablation, and risk stratification for sudden cardiac death. We also examine the risks associated with AI use, including overreliance, interpretability challenges, data bias, and generalizability. Conclusions: The integration of AI into cardiovascular care offers the potential to enhance diagnostic accuracy, tailor interventions, and support decision-making. However, the adoption of AI must be carefully balanced with clinical expertise and ethical considerations. By fostering collaboration between clinicians and AI developers, it is possible to guide the development of reliable, transparent, and effective tools that will shape the future of personalized cardiology and electrophysiology.

Background: Catheter ablation is a first-line treatment for rhythm control strategies in patients with atrial fibrillation (AF), with different energy sources available, including pulsed-field ablation (PFA), high-power short-duration radiofrequency (HPSD RF), conventional radiofrequency (RF), and cryoballoon ablation. Limited evidence exists on how different ablation techniques affect patient-reported outcomes, such as patients’ quality of life (QoL) and perceived symptoms. This study aims to assess the impact of ablation energy sources on reported QoL and symptom perception after AF ablation. Methods: The study included 148 patients who underwent catheter ablation in different centers. Patients were divided into four groups according to the energy source used. Follow-up was conducted during the 6 months post-procedure. Patients were asked to complete a 20-item questionnaire evaluating quality of life, activity resumption, recovery process, perceived symptoms, and satisfaction. Comparative analyses were performed across energy groups, anesthesia types, and anesthetic drugs. Results: PFA patients reported the highest improvement in QoL scores compared to RF, HPSD RF, and cryoablation (p < 0.001). Activity resumption and symptom relief were significantly better in the PFA group compared to others (p < 0.001). Anesthesia type and anesthetic drug influenced QoL outcomes, with patients under general anesthesia showing higher QoL scores compared to deep sedation (p < 0.001). The energy source and anesthetic drug resulted in independent predictors of QoL improvement. Conclusions: Ablation energy source could impact patients’ perceived QoL and symptom relief after AF ablation. PFA demonstrated superior performance scores in QoL and symptom perception compared to other techniques. Anesthetic drugs also play a role in patient-reported outcomes and activity resumption.

Background: Pulsed field energy has been proposed as alternative to radiofrequency energy in atrial fibrillation (AF) ablation.Objective: To compare data from studies assessing AF ablation with pulsed field ablation (PFA) versus high‐power short‐duration (HPSD) or very HPSD (vHPSD) radiofrequency ablation (RFA) in terms of AF recurrence, procedure‐related complications, fluoroscopy and procedure times.Methods: A search of online scientific libraries (from inception to October 1, 2024) was performed. Six studies were considered eligible for the meta‐analysis totaling 1190 patients of whom 568 receiving PFA and 622 receiving HPHD/vHPSD RFA.Results: In patients with paroxysmal AF (PAF), a nonsignificant reduction of AF recurrence was related to PFA as compared to HPSD/vHPSD RFA (OR 0.74 [0.50; 1.11], p = 0.14, I² 10%). In patients with persistent AF (PeAF) a nonsignificant reduction of AF recurrence was related to PFA as compared to HPSD/vHPSD RFA (OR of 0.68 [95%CI 0.35; 1.34], p‐value 0.27, I² 10%). In the overall population PFA was associated with a significant reduction of AF recurrence at follow‐up (OR 0.65 [0.47; 0.90], p = 0.009, I² 11%). No statistical differences were found among the groups in terms of total complications (OR 0.92, [0.45; 1.86], p = 0.81, I² = 27%), stroke (p = 0.78), and cardiac tamponade (p = 0.80). PFA was associated with significantly longer fluoroscopy time (WMD 8.69 [5.64; 11.75], p < 0.001, I² = 95%), but shorter procedure time (WMD −35.16 [ − 46.03; −24.28], p < 0.001, I² = 89%) compared to HPSD/vHPSD RFA.Conclusion: PFA in AF patients is associated with similar efficacy and safety profiles as compared to HPSD/vHPSD RFA.

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.