Evidence map›Paper›PMID 42023796›Full record

ArticleJACC. Advances2026

Artificial Intelligence-Assisted CMR Scanning vs Standard-of-Care: Comparative Analysis of Clinical Benefits From 6,545 Consecutive Studies.

Raymond Y Kwong, Benedikt Bernhard, Michael Jerosch-Herold, Leslie K Lee, Jon Hainer, Tuan Luu, Isabela Reis Marques, Marianna Daibes, Bob Hu

Abstract read
In one paragraph

Article in JACC. Advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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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.

Raymond Y KwongCardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA. Electronic address: rykwong@bwh.harvard.edu.
Benedikt BernhardCardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Michael Jerosch-HeroldCardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Leslie K LeeDepartment of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Jon HainerDepartment of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Tuan LuuDepartment of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Isabela Reis MarquesCardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Marianna DaibesCardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Bob HuVista AI, Palo Alto, California, USA; Department of Electrical Engineering, Stanford University, Stanford, California, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundClinical adoption of cardiovascular magnetic resonance (CMR) is hampered by procedural complexity and magnetic resonance imaging access.

objectivesThe objective of the study was to investigate the benefits of artificial intelligence (AI)-assisted CMR in clinical practice.

methodsPatients at a tertiary-care center underwent CMR via conventional, operator-directed (ODS) or AI-assisted (AIA) scanning over 3 years. Scan time, scan time variation, image quality using a validated 5-point scale, scan failure, and survival were compared between groups, stratified across CMR indications and technologists' experience.

resultsFrom 2020 to 2024, 6,080 consecutive patients (age 57 ± 17 years, 55% males) underwent 6,545 studies (1,635, 25% AIA, 4,910, 75% ODS) for 5 primary indications. Average scan time and its coefficient of variation were reduced by 23% and 31%, respectively, with AIA compared to ODS (37.9 ± 6.9 vs 49.2 ± 13.0 min; P < 0.001). Scan time reduction by AIA was consistent across indications and range of technologists' experience, and remained significant after adjusting for key demographic and clinical differences (P < 0.0001) or propensity score. Scan quality score was higher in AIA for cine images (4.09 ± 0.8 vs 4.03 ± 0.8; P < 0.001), late gadolinium enhancement (4.08 ± 0.8 vs 3.98 ± 0.7; P < 0.001), and perfusion (4.28 ± 0.6 vs 4.14 ± 0.6; P < 0.001). Scan failure leading to 90-day repeat CMR was lower in AIA compared to ODS (0.6% vs 1.4%, P < 0.001). After its introduction in 2021, AIA use progressively increased to 78% by 2023. At a median follow-up of 29 months, patient survival did not differ between groups (log-rank P = 0.500).

conclusionsIn this nonrandomized comparison, AIA improved scan time, scan time consistency, image quality, and 90-day scan failure across top CMR indications, compared to ODS.

Indexed as

artificial intelligenceCMRCMR scanfully-automatedscan time

Identifiers

PMID42023796
PMCPMC13131395

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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.