{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Svennberg E"],"funding":["British Heart Foundation","AEI","NHLBI NIH HHS","Swedish Heart and Lung Association","Healthcare Innovation Fund","Swedish Research Council","NIH","European Union","Dutch Research Council (NWO)","National Institute for Health and Care Research","NIH HHS","Stockholm County Council","MCIN"],"pagination":["euaf071"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12123071"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["27(5)"],"pubmed_abstract":["<h4>Aims</h4>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.<h4>Methods and results</h4>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 "],"journal":["Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology"],"pubmed_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."],"pmcid":["PMC12123071"],"funding_grant_id":["PID2023-148975OB-I00","R01HL174440","R01 HL174440","NIHR204553","R01 HL166759","RE/24/13003","DNR 2022-01466","R01HL166759","HI/C2/24/360038","CNS2023-143599","PID2021-128972OA-I00"],"pubmed_authors":["Millet J","Narayan SM","Hindricks G","Trayanova N","Barker J","Ernst S","Tjong FVY","Duncker D","Chatterjee NA","Man SH","Conte G","Zorzi A","Diederichsen SZ","Ramirez J","Engelhardt S","Linz D","Garcia R","Han JK","Casado-Arroyo R","Singh JP","Mahtani AU","Svennberg E","Noseworthy PA","Ng GA","Caiani EG","Tfelt Hansen J","Ghanbari H","Friedman P"],"additional_accession":[]},"is_claimable":false,"name":"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.","description":"<h4>Aims</h4>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.<h4>Methods and results</h4>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 ","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 May","modification":"2026-06-01T11:39:48.924Z","creation":"2026-04-08T11:55:46.18Z"},"accession":"S-EPMC12123071","cross_references":{"pubmed":["40163651"],"doi":["10.1093/europace/euaf071"]}}