TY - JOUR
T1 - Emerging role of artificial intelligence in cardiac electrophysiology
AU - Kabra, Rajesh
AU - Israni, Sharat
AU - Vijay, Bharat
AU - Baru, Chaitanya
AU - Mendu, Raghuveer
AU - Fellman, Mark
AU - Sridhar, Arun
AU - Mason, Pamela
AU - Cheung, Jim W.
AU - DiBiase, Luigi
AU - Mahapatra, Srijoy
AU - Kalifa, Jerome
AU - Lubitz, Steven A.
AU - Noseworthy, Peter A.
AU - Navara, Rachita
AU - McManus, David D.
AU - Cohen, Mitchell
AU - Chung, Mina K.
AU - Trayanova, Natalia
AU - Gopinathannair, Rakesh
AU - Lakkireddy, Dhanunjaya
N1 - Publisher Copyright:
© 2022 Heart Rhythm Society
PY - 2022/12
Y1 - 2022/12
N2 - Artificial intelligence (AI) and machine learning (ML) have significantly impacted the field of cardiovascular medicine, especially cardiac electrophysiology (EP), on multiple fronts. The goal of this review is to familiarize readers with the field of AI and ML and their emerging role in EP. The current review is divided into 3 sections. In the first section, we discuss the definitions and basics of AI, ML, and big data. In the second section, we discuss their application to EP in the context of detection, prediction, and management of arrhythmias. Finally, we discuss the regulatory issues, challenges, and future directions of AI in EP.
AB - Artificial intelligence (AI) and machine learning (ML) have significantly impacted the field of cardiovascular medicine, especially cardiac electrophysiology (EP), on multiple fronts. The goal of this review is to familiarize readers with the field of AI and ML and their emerging role in EP. The current review is divided into 3 sections. In the first section, we discuss the definitions and basics of AI, ML, and big data. In the second section, we discuss their application to EP in the context of detection, prediction, and management of arrhythmias. Finally, we discuss the regulatory issues, challenges, and future directions of AI in EP.
KW - Artificial intelligence
KW - Big data
KW - Cardiac electrophysiology
KW - Computational modeling
KW - Deep learning
KW - Machine learning
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U2 - 10.1016/j.cvdhj.2022.09.001
DO - 10.1016/j.cvdhj.2022.09.001
M3 - Review article
AN - SCOPUS:85141283653
SN - 2666-6936
VL - 3
SP - 263
EP - 275
JO - Cardiovascular Digital Health Journal
JF - Cardiovascular Digital Health Journal
IS - 6
ER -