TY - JOUR
T1 - Artificial Intelligence for Cardiovascular Care—Part 1
T2 - Advances: JACC Review Topic of the Week
AU - Elias, Pierre
AU - Jain, Sneha S.
AU - Poterucha, Timothy
AU - Randazzo, Michael
AU - Lopez Jimenez, Francisco
AU - Khera, Rohan
AU - Perez, Marco
AU - Ouyang, David
AU - Pirruccello, James
AU - Salerno, Michael
AU - Einstein, Andrew J.
AU - Avram, Robert
AU - Tison, Geoffrey H.
AU - Nadkarni, Girish
AU - Natarajan, Vivek
AU - Pierson, Emma
AU - Beecy, Ashley
AU - Kumaraiah, Deepa
AU - Haggerty, Chris
AU - Avari Silva, Jennifer N.
AU - Maddox, Thomas M.
N1 - Publisher Copyright:
© 2024 American College of Cardiology Foundation
PY - 2024/6/18
Y1 - 2024/6/18
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - cardiac imaging
KW - deep learning
KW - digital health
KW - innovation
KW - large language models
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85194756538&partnerID=8YFLogxK
U2 - 10.1016/j.jacc.2024.03.400
DO - 10.1016/j.jacc.2024.03.400
M3 - Review article
C2 - 38593946
AN - SCOPUS:85194756538
SN - 0735-1097
VL - 83
SP - 2472
EP - 2486
JO - Journal of the American College of Cardiology
JF - Journal of the American College of Cardiology
IS - 24
ER -