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

Svennberg, Emma ; Han, Janet K. ; Caiani, Enrico G. ; Engelhardt, Sandy ; Ernst, Sabine ; Friedman, Paul ; Garcia, Rodrigue ; Ghanbari, Hamid ; Hindricks, Gerhard ; Man, Sharon H. ; Millet, José ; Narayan, Sanjiv M. ; Ng, G. André ; Noseworthy, Peter A. ; Tjong, Fleur V.Y. ; Ramírez, Julia (Universidad de Zaragoza) ; Singh, Jagmeet P. ; Trayanova, Natalia ; Duncker, David ; Tfelt Hansen, Jacob ; Barker, Joseph ; Casado-Arroyo, Ruben ; Chatterjee, Neal A. ; Conte, Giulio ; Diederichsen, Søren Zöga ; Linz, Dominik ; Mahtani, Arun Umesh ; Zorzi, Alessandro

Resumen: Aims
Artificial intelligence (AI) has the potential to transform cardiac electrophysiology (EP), particularly in arrhythmia detection, procedural optimization, and patient outcome prediction. However, a standardized approach to reporting and understanding AI-related research in EP is lacking. This scientific statement aims to develop and apply a checklist for AI-related research reporting in EP to enhance transparency, reproducibility, and understandability in the field.

Methods and results
An AI checklist specific to EP was developed with expert input from the writing group and voted on using a modified Delphi process, leading to the development of a 29-item checklist. The checklist was subsequently applied to assess reporting practices to identify areas where improvements could be made and provide an overview of the state of the art in AI-related EP research in three domains from May 2021 until May 2024: atrial fibrillation (AF) management, sudden cardiac death (SCD), and EP lab applications. The EHRA AI checklist was applied to 31 studies in AF management, 18 studies in SCD, and 6 studies in EP lab applications. Results differed between the different domains, but in no domain reporting of a specific item exceeded 55% of included papers. Key areas such as trial registration, participant details, data handling, and training performance were underreported (<20%). The checklist application highlighted areas where reporting practices could be improved to promote clearer, more comprehensive AI research in EP.

Conclusion
The EHRA AI checklist provides a structured framework for reporting AI research in EP. Its use can improve understanding but also enhance the reproducibility and transparency of AI studies, fostering more robust and reliable integration of AI into clinical EP practice.

Idioma: Inglés

DOI: 10.1093/europace/euaf071

Año: 2025

Publicado en: Europace 27, 5 (2025), euaf071 [19 pp.]
ISSN: 1099-5129

Financiación: info:eu-repo/grantAgreement/ES/AEI/PID2021-128972OA-I00
Financiación: info:eu-repo/grantAgreement/ES/AEI/PID2023-148975OB-I00
Financiación: info:eu-repo/grantAgreement/ES/MICINN/CNS2023-143599
Financiación: info:eu-repo/grantAgreement/ES/MICINN/RYC2021-031413-I

Tipo y forma: Article (Published version)

Área (Departamento): Área Teoría Señal y Comunicac. (Dpto. Ingeniería Electrón.Com.)