Quick Dive: Artificial Intelligence in Clinical Electrophysiology

What is the reason for and aim of the publication?

The aim of the publication is to address the lack of standardization in the reporting and evaluation of AI-based research in clinical electrophysiology (EP). With the rapid growth of AI applications in arrhythmia detection, risk prediction, and procedural guidance, the field requires a structured framework to ensure transparency, reproducibility, and clinical relevance. To that end, the European Heart Rhythm Association (EHRA), in collaboration with HRS and the ESC Working Group on E-Cardiology, developed a 29-item EHRA AI checklist tailored specifically for AI studies in the field of clinical EP. This publication details the creation, validation, and application of that checklist across three major domains: atrial fibrillation (AF) management, sudden cardiac death (SCD), and EP lab applications.

What are the most important take-home messages?

1. There is considerable variability and frequent underreporting in AI-based EP studies, especially regarding trial registration, participant demographics, and data handling. A dedicated framework and standardization is essential to enhance scientific rigor.
2. Creation of the "EHRA AI checklist": A consensus-driven, Delphi-validated checklist of 29 items was created to guide authors and readers in properly reporting and assessing AI research in EP.
3. Adoption of the EHRA AI checklist can improve understanding, foster better clinical translation, and ensure AI tools are effectively applied in EP.
   

What are challenges in practical implementation – and possible solutions?

This manuscript shows inconsistent reporting standards and lack of external validation, limiting clinical trust and application of AI tools. The widespread dissemination and use of the EHRA AI checklist in EP journals and publications can overcome these inconsistencies.

What further developments on the topic are emerging?

Tools that make AI models interpretable for clinicians are gaining traction to build trust and facilitate clinical integration (explainable AI). Digital Twins and multimodal AI allow personalized simulations based on patient-specific data and are being explored to guide ablation strategies and predict outcomes. Furthermore, the integration of wearables and telemonitoring using AI algorithms can be used for arrhythmia detection and risk stratification, offering dynamic, real-time insights.

How can the EHRA AI checklist be adopted at scale to maximize its impact?

The EHRA AI checklist will now be integrated into journal submission requirements for the Europace journal. It could also be included in institutional research protocols or educational curricula for EP fellows in training and researchers.

Scientific statement: "State of the Art of Artificial Intelligence in Clinical Electrophysiology in 2025"

Reference: Svennberg E, Han JK, Caiani EG et al. State of the Art of Artificial Intelligence in Clinical Electrophysiology in 2025: A Scientific Statement of the European Heart Rhythm Association (EHRA) of the ESC, the Heart Rhythm Society (HRS), and the ESC Working Group on E-Cardiology. Europace. 2025 May 7;27(5):euaf071. doi: 10.1093/europace/euaf071. PMID: 40163651; PMCID: PMC12123071.

Document types published by the ESC, Associations, Councils, Working Groups, and ESC Committees (according to the ESC Scientific Documents Policy):