GuidelineJACC. Cardiovascular imaging2026
PRIME 2.0: Proposed Requirements for Cardiovascular Imaging-Related Multimodal-AI Evaluation: An Updated Checklist.
Guideline in JACC. Cardiovascular imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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.
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.
Who cites it
17 citing papers in PubMed.
- Deep learning-based echocardiography for cardiac assessment: a systematic literature review and meta-analysis.European heart journal. Digital health · 2026Article
- Machine Learning of Echocardiographic Left Ventricular Myocardial Radiomics for Identification of Transthyretin Amyloid Cardiomyopathy.JACC. Advances · 2026Article
- Artificial Intelligence Reporting Guidelines in Radiology: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Comparison of Artificial Intelligence-Driven Echocardiographic Assessment of Diastolic Function With the ASE Guidelines.JACC. Advances · 2026Article
- Boosting the IQ of Artificial Intelligence: Echocardiographic Big Data and Overcoming the Generalizability Gap.Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography · 2026Article
- Artificial Intelligence in Cardiovascular Ultrasound: Clinical Applications, Foundation Models, and the Path to Precision Cardiology.Journal of clinical medicine · 2026Review
- Artificial intelligence in echocardiography: a position statement from the British Society of Echocardiography.Echo research and practice · 2026Review
- Early Prediction of Heart Failure From Routine Cardiac CT Using Radiomic Phenotyping of Epicardial Fat.Journal of the American College of Cardiology · 2026Article
- Optimizing single-lead ECG axis for AI-based detection of myocardial diseases.NPJ cardiovascular health · 2026Article
- Association between deep learning-based coronary artery calcium score on non-gated chest CT and progression of chronic kidney disease: a retrospective observational cohort study.BMC medical imaging · 2026Observational
- Automated Classification of Mitral and Tricuspid Regurgitation With Explainability and Real-World Practice Experience.Journal of the American Heart Association · 2026Article
- Multisite, External Validation of an AI-Enabled ECG Algorithm for Detection of Low Ejection Fraction.JACC. Advances · 2026Article
- The Emerging Role of Artificial Intelligence in the Assessment of Valvular Heart Disease with Cardiac Imaging.Current cardiology reports · 2026Review
- Artificial Intelligence-Enabled Echocardiographic Assessment of Right Ventricular Function.medRxiv : the preprint server for health sciences · 2026Article
- Artificial intelligence in cardiovascular medicine: prevention, diagnosis, and intervention.Frontiers in artificial intelligence · 2026Review
- From precision to planetary health: the carbon cost of AI in multimodality cardiovascular imaging.European heart journal. Imaging methods and practice · 2026Article
- Artificial Intelligence and Echocardiography: From Literacy to Leadership, Redefining Roles, Responsibilities, and Sustainability in the Artificial Intelligence-driven Echocardiography Era.Journal of cardiovascular echographyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
20 authors.
Funding
Abstract
The PRIME (Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation) 2.0 checklist is an updated, domain-specific framework designed to standardize the development, evaluation, and reporting of artificial intelligence (AI) applications in cardiovascular imaging. This update specifically responds to rapid advances from traditional machine learning to deep learning, large language models, and multimodal generative AI. The updated checklist was developed through a modified Delphi process by an international panel of clinical and technical experts. In contrast to general AI reporting guidelines, it delivers detailed, practical recommendations on all critical aspects of AI research and builds upon the original 7-domain framework by incorporating cardiovascular imaging-specific complexities such as cardiac motion, imaging artifacts, and interobserver variability. By promoting transparency and rigor, PRIME 2.0 can serve as a vital resource for researchers, clinicians, peer reviewers, and journal editors working at the forefront of AI in cardiovascular imaging.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.