Evidence map›Paper›PMID 38939457›Full record

ReviewJACC. Advances2022

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 and 2 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in JACC. Advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

30 citing papers in PubMed.

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  19. The molecular mechanisms of cardiac development and related diseases.Signal transduction and targeted therapy · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Pei-Ni JoneSection of Pediatric Cardiology, Department of Pediatrics, Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
Addison GearhartDepartment of Cardiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Howard LeiDivision of Pediatric Cardiology, Children's Hospital of Orange County, Orange, California, USA.
Fuyong XingDepartment of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.
Jai NaharDepartment of Cardiology, Children's National Hospital, Washington, DC, USA.
Francisco Lopez-JimenezDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
Gerhard-Paul DillerDepartment of Cardiology III-Adult Congenital and Valvular Heart Disease, University Hospital Muenster, Muenster, Germany.
Ariane MarelliMcGill Adult Unit for Congenital Heart Disease Excellence, Department of Medicine, McGill University, Montréal, Québec, Canada.
Laura WilsonDepartment of Pediatrics, University of Florida-Congenital Heart Center, Gainesville, Florida, USA.
Arwa SaidiDepartment of Pediatrics, University of Florida-Congenital Heart Center, Gainesville, Florida, USA.
David ChoDepartment of Cardiology, University of California at Los Angeles, Los Angeles, California, USA.
Anthony C ChangDivision of Pediatric Cardiology, Children's Hospital of Orange County, Orange, California, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

adult congenital heart diseaseartificial intelligencecardiac imagingcongenital heart diseasemachine learningoutcome prediction

Identifiers

PMID38939457
PMCPMC11198540

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.