Evidence map›Paper›PMID 41241006›Full record

ArticleJournal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance2025

Cardiac magnetic resonance imaging-large language model Meta AI: a finetuned large language model for identifying findings and associated attributes in cardiac magnetic resonance imaging reports.

Michelle Z Fang, Makiya Nakashima, Kailash Singh, Eileen Galvani, Xiaotan Sun, Sharmeen Sorathia, Kevin Dorocak, Deborah Kwon, Christopher Nguyen, David Chen

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In one paragraph

Article in Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

10 authors.

Michelle Z FangCleveland Clinic Lerner College of Medicine at Case Western, Cleveland, Ohio, USA.
Makiya NakashimaHeart Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA; Cardiovascular Innovation Research Center, Cleveland Clinic, Cleveland, Ohio, USA.
Kailash SinghCleveland Clinic Lerner College of Medicine at Case Western, Cleveland, Ohio, USA.
Eileen GalvaniHeart Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA.
Xiaotan SunHeart Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA; Cardiovascular Innovation Research Center, Cleveland Clinic, Cleveland, Ohio, USA.
Sharmeen SorathiaHeart Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA.
Kevin DorocakHeart Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA.
Deborah KwonHeart Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA; Cardiovascular Innovation Research Center, Cleveland Clinic, Cleveland, Ohio, USA.
Christopher NguyenHeart Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA; Cardiovascular Innovation Research Center, Cleveland Clinic, Cleveland, Ohio, USA.
David ChenCardiovascular Innovation Research Center, Cleveland Clinic, Cleveland, Ohio, USA; Diagnostics Institute, Cleveland Clinic, Cleveland, Ohio, USA. Electronic address: CHEND3@ccf.org.

Funding

Improving Phenotypic Classification and Prediction of Treatment Outcomes in Patients with Non-ischemic Cardiomyopathy and Functional Mitral RegurgitationR01HL170090 · NHLBI · CLEVELAND CLINIC LERNER COM-CWRU · PI DEBORAH KWON · 2023 to 2026
$3.1M
NHLBI NIH HHS R01 HL170090
6 · The paper itself

Abstract

backgroundCardiac magnetic resonance imaging (CMR) studies contain a wealth of information on a patient's cardiovascular status. The ability to extract this data from free-text reports could serve to automate clinical decision support tools and generate data for retrospective clinical knowledge discovery, and clinical operational purposes. Few studies have examined the automatic extraction of data from free-text CMR reports, and the existing studies that do have key limitations, including small sample size and disease-specific data extraction. Existing studies also fail to extract features associated with the cardiovascular conditions that reflect nuances in natural language, such as uncertainty, severity, subtype, and anatomical locations of the condition. The goal of this study was to build a broad named entity recognition model to automatically extract a broad variety of common CMR findings and their associated attributes from CMR reports.

methodsWe fine-tuned a Large Language Model Meta AI (LLaMA) model trained to identify 34 cardiovascular conditions and their associated attributes, including certainty, severity, location, and subtype of the condition. This model was trained on 1778 MRI reports and tested on 397 reports in an held-out test set and another 428 reports from another site in our hospital system with independent radiology practice and scanners.

resultsOur model shows robust performance in predicting the mention of the 31 cardiovascular conditions (average F1=0.85). It also showed strong performance predicting attributes, including certainty (average F1=0.97) and severity (average F1=0.97). Model performance on the external validation set was generally slightly lower than the internal validation set, but performance was still strong (average F1=0.78 for mention, 0.97 for certainty, and 0.96 for severity).

conclusionCMR-LLaMA has strong performance identifying a variety of concept mentions and moderate accuracies in extracting a selection of other associated attributes. NLP models can be used to automate the extraction of data from CMR reports to potentially assist with clinical and research workflow.

Indexed as

Artificial IntelligenceCardiovascular DiseasesData MiningHeart DiseasesMagnetic Resonance ImagingNatural Language ProcessingHumansLarge Language ModelsPredictive Value of TestsSeverity of Illness IndexDiagnosis extractionFindings extractionGround-truth labelingNamed entity recognitionNatural language processingRadiology report analysisTransformers

Identifiers

PMID41241006
PMCPMC12766592

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Registered trials

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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.