Imperial College London
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ProfessorNicholasPeters

Faculty of Medicine, National Heart & Lung Institute

Professor of Cardiac Electrophysiology
 
 
 
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Contact

 

+44 (0)20 7594 1880n.peters Website

 
 
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Assistant

 

Ms Anastasija Schmidt +44 (0)20 7594 1880

 
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Location

 

NHLI officesSir Michael Uren HubWhite City Campus

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Summary

 

Publications

Citation

BibTex format

@article{Feeny:2020:10.1161/CIRCEP.119.007952,
author = {Feeny, AK and Chung, MK and Madabhushi, A and Attia, ZI and Cikes, M and Firouznia, M and Friedman, PA and Kalscheur, MM and Kapa, S and Narayan, SM and Noseworthy, PA and Passman, RS and Perez, MV and Peters, NS and Piccini, JP and Tarakji, KG and Thomas, SA and Trayanova, NA and Turakhia, MP and Wang, PJ},
doi = {10.1161/CIRCEP.119.007952},
journal = {Circulation: Arrhythmia and Electrophysiology},
pages = {1--18},
title = {Artificial intelligence and machine learning in arrhythmias and cardiac electrophysiology},
url = {http://dx.doi.org/10.1161/CIRCEP.119.007952},
volume = {13},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Artificial intelligence (AI) and machine learning (ML) in medicine are currently areas of intense exploration, showing potential to automate human tasks and even perform tasks beyond human capabilities. Literacy and understanding of AI/ML methods are becoming increasingly important to researchers and clinicians. The first objective of this review is to provide the novice reader with literacy of AI/ML methods and provide a foundation for how one might conduct an ML study. We provide a technical overview of some of the most commonly used terms, techniques, and challenges in AI/ML studies, with reference to recent studies in cardiac electrophysiology to illustrate key points. The second objective of this review is to use examples from recent literature to discuss how AI and ML are changing clinical practice and research in cardiac electrophysiology, with emphasis on disease detection and diagnosis, prediction of patient outcomes, and novel characterization of disease. The final objective is to highlight important considerations and challenges for appropriate validation, adoption, and deployment of AI technologies into clinical practice.
AU  - Feeny,AK
AU  - Chung,MK
AU  - Madabhushi,A
AU  - Attia,ZI
AU  - Cikes,M
AU  - Firouznia,M
AU  - Friedman,PA
AU  - Kalscheur,MM
AU  - Kapa,S
AU  - Narayan,SM
AU  - Noseworthy,PA
AU  - Passman,RS
AU  - Perez,MV
AU  - Peters,NS
AU  - Piccini,JP
AU  - Tarakji,KG
AU  - Thomas,SA
AU  - Trayanova,NA
AU  - Turakhia,MP
AU  - Wang,PJ
DO  - 10.1161/CIRCEP.119.007952
EP  - 18
PY  - 2020///
SN  - 1941-3084
SP  - 1
TI  - Artificial intelligence and machine learning in arrhythmias and cardiac electrophysiology
T2  - Circulation: Arrhythmia and Electrophysiology
UR  - http://dx.doi.org/10.1161/CIRCEP.119.007952
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000565478900003&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://www.ahajournals.org/doi/full/10.1161/CIRCEP.119.007952
VL  - 13
ER  -
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