TY - JOUR
T1 - State of the Art of Artificial Intelligence in Clinical Electrophysiology in 2025
T2 - 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
AU - Svennberg, Emma
AU - Han, Janet K.
AU - Caiani, Enrico G.
AU - Engelhardt, Sandy
AU - Ernst, Sabine
AU - Friedman, Paul
AU - Garcia, Rodrigue
AU - Ghanbari, Hamid
AU - Hindricks, Gerhard
AU - Man, Sharon H.
AU - Millet, José
AU - Narayan, Sanjiv M.
AU - Ng, G. André
AU - Noseworthy, Peter A.
AU - Tjong, Fleur V. Y.
AU - Ramírez, Julia
AU - Singh, Jagmeet P.
AU - Trayanova, Natalia
AU - Duncker, David
AU - Tfelt Hansen, Jacob
AU - Barker, Joseph
AU - Casado-Arroyo, Ruben
AU - Chatterjee, Neal A.
AU - Conte, Giulio
AU - Diederichsen, S. ren Z. ga
AU - Linz, Dominik
AU - Mahtani, Arun Umesh
AU - Zorzi, Alessandro
N1 - Publisher Copyright:
© 2025 the European Society of Cardiology.
PY - 2025/5/1
Y1 - 2025/5/1
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Checklist
KW - Electrophysiology
KW - Machine learning
UR - https://www.scopus.com/pages/publications/105007005507
U2 - 10.1093/europace/euaf071
DO - 10.1093/europace/euaf071
M3 - Article
C2 - 40163651
SN - 1099-5129
VL - 27
JO - Europace
JF - Europace
IS - 5
M1 - euaf071
ER -