Artificial Intelligence for Cardiovascular Care-Part 1: Advances

被引：35

作者：

Elias, Pierre ^{[1
,2
]}

Jain, Sneha S. ^{[3
]}

Poterucha, Timothy ^{[1
]}

Randazzo, Michael ^{[4
]}

Jimenez, Francisco Lopez ^{[5
]}

Khera, Rohan ^{[6
]}

Perez, Marco ^{[3
]}

Ouyang, David ^{[7
]}

Pirruccello, James ^{[2
,8
]}

Salerno, Michael ^{[3
]}

Einstein, Andrew J. ^{[1
]}

Avram, Robert ^{[9
]}

Tison, Geoffrey H. ^{[8
]}

Nadkarni, Girish ^{[10
]}

Natarajan, Vivek ^{[11
]}

Pierson, Emma ^{[12
]}

Beecy, Ashley ^{[13
,14
]}

Kumaraiah, Deepa ^{[1
,13
]}

Haggerty, Chris ^{[2
,13
]}

Silva, Jennifer N. Avari ^{[15
]}

Maddox, Thomas M. ^{[15
]}

机构：

[1] Columbia Univ, Irving Med Ctr, Seymour Paul & Gloria Milstein Div Cardiol, New York, NY USA

[2] Columbia Univ, Irving Med Ctr, Dept Biomed Informat, New York, NY USA

[3] Stanford Univ, Sch Med, Div Cardiol, Palo Alto, CA USA

[4] Univ Chicago, Med Ctr, Div Cardiol, Chicago, IL USA

[5] Mayo Clin, Coll Med, Dept Cardiol, Rochester, MN USA

[6] Yale Sch Med, Div Cardiol, New Haven, CT USA

[7] Cedars Sinai Med Ctr, Div Cardiol, Los Angeles, CA USA

[8] Univ Calif San Francisco, Div Cardiol, San Francisco, CA USA

[9] Montreal Heart Inst, Div Cardiol, Montreal, PQ, Canada

[10] Icahn Sch Med Mt Sinai, New York, NY USA

[11] Google Hlth, Mountain View, CA USA

[12] Cornell Tech, Dept Comp Sci, New York, NY USA

[13] New York Presbyterian Hlth Syst, New York, NY USA

[14] Weill Cornell Med Coll, Div Cardiol, New York, NY USA

[15] Washington Univ, Sch Med, Div Cardiol, St Louis, MO USA

来源：

JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY | 2024年 / 83卷 / 24期

基金：

美国国家卫生研究院;

关键词：

artificial intelligence; cardiac imaging; deep learning; digital health; innovation; large language models; machine learning; MAGNETIC-RESONANCE; ATRIAL-FIBRILLATION; LEARNING ALGORITHM; EJECTION FRACTION; HEART-FAILURE; ELECTROCARDIOGRAM; DIAGNOSIS; STENOSIS; CLASSIFICATION; IDENTIFICATION;

D O I：

10.1016/j.jacc.2024.03.400

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements in diagnosis, treatment, and outcomes. Innovations to date focus on automating measurements, enhancing image quality, and detecting diseases using novel methods. Applications span wearables, electrocardiograms, echocardiography, angiography, genetics, and more. AI models detect diseases from electrocardiograms at accuracy not previously achieved by technology or human experts, including reduced ejection fraction, valvular heart disease, and other cardiomyopathies. However, AI 's unique characteristics necessitate rigorous validation by addressing training methods, real-world efficacy, equity concerns, and long-term reliability. Despite an exponentially growing number of studies in cardiovascular AI, trials showing improvement in outcomes remain lacking. A number are currently underway. Embracing this rapidly evolving technology while setting a high evaluation benchmark will be crucial for cardiology to leverage AI to enhance patient care and the provider experience. (c) 2024 by the American College of Cardiology Foundation.

引用

页码：2472 / 2486

页数：15

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