Artificial Intelligence in Congenital Heart Disease: Current State and Prospects

Pei Ni Jone; Addison Gearhart; Howard Lei; Fuyong Xing; Jai Nahar; Francisco Lopez-Jimenez; Gerhard Paul Diller; Ariane Marelli; Laura Wilson; Arwa Saidi; David Cho; Anthony C. Chang

doi:10.1016/j.jacadv.2022.100153

Artificial Intelligence in Congenital Heart Disease: Current State and Prospects

Pei Ni Jone, Addison Gearhart, Howard Lei, Fuyong Xing, Jai Nahar, Francisco Lopez-Jimenez, Gerhard Paul Diller, Ariane Marelli, Laura Wilson, Arwa Saidi, David Cho, Anthony C. Chang

Cardiovascular Medicine

Research output: Contribution to journal › Review article › peer-review

Abstract

The current era of big data offers a wealth of new opportunities for clinicians to leverage artificial intelligence to optimize care for pediatric and adult patients with a congenital heart disease. At present, there is a significant underutilization of artificial intelligence in the clinical setting for the diagnosis, prognosis, and management of congenital heart disease patients. This document is a call to action and will describe the current state of artificial intelligence in congenital heart disease, review challenges, discuss opportunities, and focus on the top priorities of artificial intelligence–based deployment in congenital heart disease.

Original language	English (US)
Article number	100153
Journal	JACC: Advances
Volume	1
Issue number	5
DOIs	https://doi.org/10.1016/j.jacadv.2022.100153
State	Published - Dec 2022

Keywords

adult congenital heart disease
artificial intelligence
cardiac imaging
congenital heart disease
machine learning
outcome prediction

ASJC Scopus subject areas

Cardiology and Cardiovascular Medicine
Dentistry (miscellaneous)

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10.1016/j.jacadv.2022.100153

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title = "Artificial Intelligence in Congenital Heart Disease: Current State and Prospects",

abstract = "The current era of big data offers a wealth of new opportunities for clinicians to leverage artificial intelligence to optimize care for pediatric and adult patients with a congenital heart disease. At present, there is a significant underutilization of artificial intelligence in the clinical setting for the diagnosis, prognosis, and management of congenital heart disease patients. This document is a call to action and will describe the current state of artificial intelligence in congenital heart disease, review challenges, discuss opportunities, and focus on the top priorities of artificial intelligence–based deployment in congenital heart disease.",

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AU - Jone, Pei Ni

AU - Gearhart, Addison

AU - Lei, Howard

AU - Xing, Fuyong

AU - Nahar, Jai

AU - Lopez-Jimenez, Francisco

AU - Diller, Gerhard Paul

AU - Marelli, Ariane

AU - Wilson, Laura

AU - Saidi, Arwa

AU - Cho, David

AU - Chang, Anthony C.

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AB - The current era of big data offers a wealth of new opportunities for clinicians to leverage artificial intelligence to optimize care for pediatric and adult patients with a congenital heart disease. At present, there is a significant underutilization of artificial intelligence in the clinical setting for the diagnosis, prognosis, and management of congenital heart disease patients. This document is a call to action and will describe the current state of artificial intelligence in congenital heart disease, review challenges, discuss opportunities, and focus on the top priorities of artificial intelligence–based deployment in congenital heart disease.

KW - adult congenital heart disease

KW - artificial intelligence

KW - cardiac imaging

KW - congenital heart disease

KW - machine learning

KW - outcome prediction

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Artificial Intelligence in Congenital Heart Disease: Current State and Prospects

Abstract

Keywords

ASJC Scopus subject areas

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