Evidence map›Paper›PMID 42126752›Full record

ReviewCurrent heart failure reports2026

Emerging Artificial Intelligence Tools for the Screening of Structural and Valvular Heart Disease.

Yasmine Abbaoui, Alexis Nolin-Lapalme, Julianne Morisset, Ines El Adib, Philippe Genereux, Timothy J Poterucha, Pierre Elias, Xioaxi Yao, Robert Avram

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Current heart failure reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Yasmine AbbaouiFaculty of Medicine, University of Montreal, Montreal, QC, Canada.ORCID http://orcid.org/0009-0000-9853-3435
Alexis Nolin-LapalmeFaculty of Medicine, University of Montreal, Montreal, QC, Canada.ORCID http://orcid.org/0000-0001-7919-2508
Julianne MorissetFaculty of Medicine, University of Montreal, Montreal, QC, Canada.
Ines El AdibFaculty of Medicine, University of Montreal, Montreal, QC, Canada.ORCID http://orcid.org/0009-0005-6497-9857
Philippe GenereuxGagnon Cardiovascular Institute, Morristown Medical Center, Morristown, NJ, USA.ORCID http://orcid.org/0000-0002-5507-3712
Timothy J PoteruchaDepartment of Cardiology, Mayo Clinic, Rochester, MN, USA.ORCID http://orcid.org/0000-0001-7284-3937
Pierre EliasDepartment of Biomedical Informatics, Columbia University, New York, NY, USA.ORCID http://orcid.org/0000-0002-9643-3024
Xioaxi YaoDivision of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA.ORCID http://orcid.org/0000-0001-9906-7106
Robert AvramFaculty of Medicine, University of Montreal, Montreal, QC, Canada. Robert.avram.md@gmail.com.ORCID http://orcid.org/0000-0002-8490-0270

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewStructural heart disease (SHD) encompasses diseases involving the heart valves, chambers, walls, and muscles. Current diagnostic methods have limited accessibility and predictive value. This review aims to present recent advances in artificial intelligence (AI)-guided tools in the screening of SHD and valvular heart disease (VHD), and to present challenges and opportunities for their use in clinical practice. RECENT

findingsAI-guided models trained on ECGs, chest X-rays, and coronary artery calcium scans have a high accuracy in the diagnosis of SHD, heart failure, low left ventricular ejection fraction, and VHD. Some of these models can highlight the signals that influence their predictions, improving explainability. The use of AI in screening for SHD and VHD could lead to earlier diagnosis, enhanced accuracy, and better accessibility. However, outcome data on earlier diagnosis using these tools is required before broad deployment.

Indexed as

Artificial IntelligenceHeart Valve DiseasesMass ScreeningHumansArtificial intelligenceHeart failureLow left ventricular ejection fractionScreeningStructural heart diseaseValvular heart disease

Identifiers

PMID42126752
PMCPMC13171939

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.