Rajesh Kabra MD, FHRS , Sharat Israni PhD , Bharat Vijay MS , Chaitanya Baru PhD , Raghuveer Mendu BTech , Mark Fellman BS , Arun Sridhar MD, FHRS , Pamela Mason MD, FHRS , Jim W. Cheung MD, FHRS , Luigi DiBiase MD, PhD, FHRS , Srijoy Mahapatra MD, FHRS , Jerome Kalifa MD, PhD , Steven A. Lubitz MD , Peter A. Noseworthy MD, FHRS , Rachita Navara MD , David D. McManus MD, FHRS , Mitchell Cohen MD , Mina K. Chung MD, FHRS , Natalia Trayanova PhD, FHRS , Rakesh Gopinathannair MD, FHRS , Dhanunjaya Lakkireddy MD, FHRS
{"title":"人工智能在心脏电生理中的新作用","authors":"Rajesh Kabra MD, FHRS , Sharat Israni PhD , Bharat Vijay MS , Chaitanya Baru PhD , Raghuveer Mendu BTech , Mark Fellman BS , Arun Sridhar MD, FHRS , Pamela Mason MD, FHRS , Jim W. Cheung MD, FHRS , Luigi DiBiase MD, PhD, FHRS , Srijoy Mahapatra MD, FHRS , Jerome Kalifa MD, PhD , Steven A. Lubitz MD , Peter A. Noseworthy MD, FHRS , Rachita Navara MD , David D. McManus MD, FHRS , Mitchell Cohen MD , Mina K. Chung MD, FHRS , Natalia Trayanova PhD, FHRS , Rakesh Gopinathannair MD, FHRS , Dhanunjaya Lakkireddy MD, FHRS","doi":"10.1016/j.cvdhj.2022.09.001","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":72527,"journal":{"name":"Cardiovascular digital health journal","volume":"3 6","pages":"Pages 263-275"},"PeriodicalIF":2.6000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9795267/pdf/","citationCount":"6","resultStr":"{\"title\":\"Emerging role of artificial intelligence in cardiac electrophysiology\",\"authors\":\"Rajesh Kabra MD, FHRS , Sharat Israni PhD , Bharat Vijay MS , Chaitanya Baru PhD , Raghuveer Mendu BTech , Mark Fellman BS , Arun Sridhar MD, FHRS , Pamela Mason MD, FHRS , Jim W. Cheung MD, FHRS , Luigi DiBiase MD, PhD, FHRS , Srijoy Mahapatra MD, FHRS , Jerome Kalifa MD, PhD , Steven A. Lubitz MD , Peter A. Noseworthy MD, FHRS , Rachita Navara MD , David D. McManus MD, FHRS , Mitchell Cohen MD , Mina K. Chung MD, FHRS , Natalia Trayanova PhD, FHRS , Rakesh Gopinathannair MD, FHRS , Dhanunjaya Lakkireddy MD, FHRS\",\"doi\":\"10.1016/j.cvdhj.2022.09.001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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.</p></div>\",\"PeriodicalId\":72527,\"journal\":{\"name\":\"Cardiovascular digital health journal\",\"volume\":\"3 6\",\"pages\":\"Pages 263-275\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2022-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9795267/pdf/\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cardiovascular digital health journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666693622001530\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CARDIAC & CARDIOVASCULAR SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cardiovascular digital health journal","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666693622001530","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
Emerging role of artificial intelligence in cardiac electrophysiology
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.
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