Evidence map›Paper›PMID 42111101›Full record

ReviewJournal of the Society for Cardiovascular Angiography & Interventions2026

Clinical Applications of Artificial Intelligence in Structural Heart Disease.

Haytham Allaham, Diljon Chahal, Mukta Srivastava, Seyed Hossein Aalaei-Andabili, Anuj Gupta

Abstract readReview
In one paragraph

Review in Journal of the Society for Cardiovascular Angiography & Interventions, 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

5 authors.

Haytham AllahamDivision of Cardiovascular Medicine, University of Maryland Medical Center, Baltimore, Maryland.
Diljon ChahalDivision of Cardiovascular Medicine, University of Maryland Medical Center, Baltimore, Maryland.
Mukta SrivastavaDivision of Cardiovascular Medicine, University of Maryland Medical Center, Baltimore, Maryland.
Seyed Hossein Aalaei-AndabiliDivision of Cardiovascular Medicine, University of Maryland Medical Center, Baltimore, Maryland.
Anuj GuptaDivision of Cardiovascular Medicine, University of Maryland Medical Center, Baltimore, Maryland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Transcatheter cardiac interventions have advanced substantially over the past decade, providing less invasive therapeutic options for an expanding spectrum of structural heart disease. As procedural complexity increases, artificial intelligence (AI) has emerged as a complementary tool to support clinical decision making. Early studies demonstrated that AI can automate imaging analysis, quantify cardiac anatomy, and assess hemodynamics with greater accuracy, efficiency, and reproducibility than conventional methods. This comprehensive review summarizes current clinical applications of AI in structural heart disease, focusing on its role in diagnosis, assessment of disease progression, risk stratification, procedural planning, and prediction of clinical outcomes.

Indexed as

artificial intelligenceatrial fibrillationmitraclipstructural heart diseasetranscatheter aortic valve replacementtriclip

Identifiers

PMID42111101
PMCPMC13154643

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.