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: 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.

It has been widely reported that the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) attaches human cells by using the Angiotensin Converting Enzyme 2 (ACE2) receptor, but vascular impairment described during coronavirus disease 2019 (COVID-19) infection is primarily due to the direct involvement of the endothelial cells by the virus or secondarily to the inflammatory host response is currently unknown. We therefore aimed to demonstrate in vivo the presence of endothelial dysfunction in six COVID-19 patients without cardiovascular risk factors or pre-existing cardiac condition, using the Endo-PAT 2000, a device able to measure endothelial vasodilation function in a rapid and non-invasive way. Four patients were positive for endothelial dysfunction, with RHI values between 1.13-1.56 (average value 1.32, normal values >1.67); in one of the two negative patients the reported RHI value was slightly above the cutoff (1.72). Our findings confirm that COVID-19 patients are at higher risk of developing endothelial dysfunction. In addition, our results demonstrate that endothelial impairment may occur even in the absence of cardiovascular risk factors.

The history of sodium-glucose cotransporter 2 inhibitors (SGLT2i) is so long and started in 1835 when Petersen extracted a compound called phlorizin from apple tree bark. About fifty years later, von Mering discovered its glucosuric properties. In the 1980s, it was discovered that the glucosuria resulted from inhibition by phlorizin of glucose reabsorption by the renal tubules, which lowered blood glucose levels in diabetic rats. Nowadays, beyond their glucose-lowering effects, growing evidence suggests significant cardiorenal benefits associated with SGLT2i therapy. Indeed, several clinical trials, including landmark studies such as EMPA-REG OUTCOME, CANVAS Program, and DECLARE-TIMI 58, have demonstrated robust reductions in cardiovascular events, particularly heart failure hospitalizations and cardiovascular mortality, among patients treated with SGLT2i. However, subsequent trials showed that SGLT2i benefits extend beyond the diabetic population, encompassing individuals with and without diabetes. Additionally, SGLT2i exhibit nephroprotective effects, manifesting as a slowing of the progression of chronic kidney disease and a reduction in the risk of end-stage kidney disease. The mechanisms underlying the cardiorenal benefits of SGLT2i are multifactorial and include improvements in glycemic control, reduction in arterial stiffness, modulation of inflammation and oxidative stress, reduction of intraglomerular pression and promotion of natriuresis and diuresis through inhibition of SGLT2 in the luminal brush border of the first segments of the proximal kidney tubule. This narrative review aims to explore the cardiorenal outcomes of SGLT2i, encompassing their mechanisms of action, clinical evidence, safety profile, and implications for clinical practice.

In the latest ESC/EACTS Guidelines for the Management of Valvular Heart Disease, right ventricular dilatation and dysfunction, severe pulmonary hypertension and tricuspid annulus dilatation were reported to be the most important parameters to consider in patient selection for tricuspid valve interventions. Indeed, comprehensive right ventricular assessment is crucial in patients with severe tricuspid regurgitation who may benefit from transcatheter or surgical procedures. However, the only guideline parameter considered for intervention has been tricuspid annular dilatation in the presence of at least mild to moderate tricuspid regurgitation, with no other right ventricular markers used in the decision-making process for invasive treatment. Notably, challenges in the assessment of right ventricular function may limit establishing thresholds for defining right ventricular dysfunction. The aim of this review is to summarize current evidence on the prognostic significance of right ventricular function in patients with tricuspid regurgitation undergoing percutaneous or surgical interventions.

Lots of meta-analysis emphasize that a great number of hospitalized patients with moderate and severe forms of COVID-19 developed acute myocardial damage, defined as an increase of cardiac biomarkers, such N-terminal pro–B-type natriuretic peptide (NT-pro-BNP), creatine kinase-myocardial band (CK-MB) and of all type of troponins. The highest mortality rate is related with progressively increasing biomarkers levels and with a history of cardiovascular disease. In fact, the biomarkers dosage should be considered as a prognostic marker in all patients with COVID-19 disease at admission, during hospitalization and in the case of clinical deterioration. The purpose of this review is to evaluate cardiovascular prognostic factors in COVID-19 disease throughout the analysis of cardiac biomarkers to early identify the most serious patients and to optimize their outcomes.