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

EP Europace Journal

31 March 2025
Organised by: Logo
ESC Journals ARRHYTHMIAS AND DEVICE THERAPY Atrial Fibrillation (AF)

Abstract

AbstractAims

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.

Contributors

Emma Svennberg
Emma Svennberg

Author

Karolinska Institute Stockholm , Sweden

Janet K Han
Janet K Han

Author

VA Greater Los Angeles Healthcare System Los Angeles , United States of America

Enrico G Caiani
Enrico G Caiani

Author

Polytechnic of Milan Milan , Italy

Sandy Engelhardt
Sandy Engelhardt

Author

University Hospital Heidelberg Heidelberg , Germany

Sabine Ernst
Sabine Ernst

Author

Royal Brompton Hospital London , United Kingdom of Great Britain & Northern Ireland

Rodrigue Garcia
Rodrigue Garcia

Author

University Hospital of Poitiers Poitiers , France

José Millet
José Millet

Author

Universitat Politecnica de Valencia Valencia , Spain

Sanjiv M Narayan
Sanjiv M Narayan

Author

Stanford University School of Medicine Palo Alto , United States of America

G André Ng
G André Ng

Author

University of Leicester Leicester , United Kingdom of Great Britain & Northern Ireland

Peter A Noseworthy
Peter A Noseworthy

Author

Mayo Clinic Rochester , United States of America

David Duncker
David Duncker

Author

Hannover Heart Rhythm Center Hannover , Germany

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