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

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

doi:10.1093/europace/euaf071

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

Emma Svennberg^*, Janet K. Han, Enrico G. Caiani, Sandy Engelhardt, Sabine Ernst, Paul Friedman, Rodrigue Garcia, Hamid Ghanbari, Gerhard Hindricks, Sharon H. Man, Jose Millet, Sanjiv M. Narayan, G. Andre Ng, Peter A. Noseworthy, Fleur V. Y. Tjong, Julia Ramirez, Jagmeet P. Singh, Natalia Trayanova, David Duncker, Jacob Tfelt HansenJoseph Barker, Ruben Casado-Arroyo, Neal A. Chatterjee, Giulio Conte, Soren Zoega Diederichsen, Dominik Linz, Arun Umesh Mahtani, Alessandro Zorzi

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

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.[GRAPHICS].

Original language	English
Article number	euaf071
Number of pages	19
Journal	EP Europace
Volume	27
Issue number	5
DOIs	https://doi.org/10.1093/europace/euaf071
Publication status	Published - 1 May 2025

Keywords

Artificial intelligence
Checklist
Machine learning
Electrophysiology
SUDDEN CARDIAC DEATH
ATRIAL-FIBRILLATION
SINUS RHYTHM
PREDICTION
ALGORITHM
ELECTROCARDIOGRAM
DYSFUNCTION
VALIDATION
ABLATION
DEVICES

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Svennberg, E., Han, J. K., Caiani, E. G., Engelhardt, S., Ernst, S., Friedman, P., Garcia, R., Ghanbari, H., Hindricks, G., Man, S. H., Millet, J., Narayan, S. M., Ng, G. A., Noseworthy, P. A., Tjong, F. V. Y., Ramirez, J., Singh, J. P., Trayanova, N., Duncker, D., ... Zorzi, A. (2025). 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, 27(5), Article euaf071. https://doi.org/10.1093/europace/euaf071

@article{2d5d919099884385b9914b8a8f263148,

title = "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",

abstract = "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.[GRAPHICS].",

keywords = "Artificial intelligence, Checklist, Machine learning, Electrophysiology, SUDDEN CARDIAC DEATH, ATRIAL-FIBRILLATION, SINUS RHYTHM, PREDICTION, ALGORITHM, ELECTROCARDIOGRAM, DYSFUNCTION, VALIDATION, ABLATION, DEVICES",

author = "Emma Svennberg and Han, \{Janet K.\} and Caiani, \{Enrico G.\} and Sandy Engelhardt and Sabine Ernst and Paul Friedman and Rodrigue Garcia and Hamid Ghanbari and Gerhard Hindricks and Man, \{Sharon H.\} and Jose Millet and Narayan, \{Sanjiv M.\} and Ng, \{G. Andre\} and Noseworthy, \{Peter A.\} and Tjong, \{Fleur V. Y.\} and Julia Ramirez and Singh, \{Jagmeet P.\} and Natalia Trayanova and David Duncker and \{Tfelt Hansen\}, Jacob and Joseph Barker and Ruben Casado-Arroyo and Chatterjee, \{Neal A.\} and Giulio Conte and Diederichsen, \{Soren Zoega\} and Dominik Linz and Mahtani, \{Arun Umesh\} and Alessandro Zorzi",

year = "2025",

month = may,

day = "1",

doi = "10.1093/europace/euaf071",

language = "English",

volume = "27",

journal = "EP Europace",

issn = "1099-5129",

publisher = "Oxford University Press",

number = "5",

}

Svennberg, E, Han, JK, Caiani, EG, Engelhardt, S, Ernst, S, Friedman, P, Garcia, R, Ghanbari, H, Hindricks, G, Man, SH, Millet, J, Narayan, SM, Ng, GA, Noseworthy, PA, Tjong, FVY, Ramirez, J, Singh, JP, Trayanova, N, Duncker, D, Tfelt Hansen, J, Barker, J, Casado-Arroyo, R, Chatterjee, NA, Conte, G, Diederichsen, SZ, Linz, D, Mahtani, AU & Zorzi, A 2025, '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, vol. 27, no. 5, euaf071. https://doi.org/10.1093/europace/euaf071

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. et al.
In: EP Europace, Vol. 27, No. 5, euaf071, 01.05.2025.

Research output: Contribution to journal › Article › Academic › peer-review

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, Jose

AU - Narayan, Sanjiv M.

AU - Ng, G. Andre

AU - Noseworthy, Peter A.

AU - Tjong, Fleur V. Y.

AU - Ramirez, 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, Soren Zoega

AU - Linz, Dominik

AU - Mahtani, Arun Umesh

AU - Zorzi, Alessandro

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.[GRAPHICS].

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.[GRAPHICS].

KW - Artificial intelligence

KW - Checklist

KW - Machine learning

KW - Electrophysiology

KW - SUDDEN CARDIAC DEATH

KW - ATRIAL-FIBRILLATION

KW - SINUS RHYTHM

KW - PREDICTION

KW - ALGORITHM

KW - ELECTROCARDIOGRAM

KW - DYSFUNCTION

KW - VALIDATION

KW - ABLATION

KW - DEVICES

U2 - 10.1093/europace/euaf071

DO - 10.1093/europace/euaf071

M3 - Article

SN - 1099-5129

VL - 27

JO - EP Europace

JF - EP Europace

IS - 5

M1 - euaf071

ER -

Svennberg E, Han JK, Caiani EG, Engelhardt S, Ernst S, Friedman P 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. EP Europace. 2025 May 1;27(5):euaf071. doi: 10.1093/europace/euaf071