ArticleNature medicine2024
Vision-language foundation model for echocardiogram interpretation.
Article in Nature medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 106 papers, 2 of them syntheses that pooled 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.
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
106 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- PRIME 2.0: Proposed Requirements for Cardiovascular Imaging-Related Multimodal-AI Evaluation: An Updated Checklist.JACC. Cardiovascular imaging · 2026Guideline
- Automated and reference methods for the calculation of left ventricular outflow tract velocity time integral or ejection fraction by non-cardiologists: a systematic review on the agreement of the two methods.Journal of clinical monitoring and computing · 2025Pooled it
- Language-supervised electrocardiogram interpretation with vision transformer.Health information science and systems · 2026Article
- A multiview video-text model detects obstructive coronary stenosis and predicts 1-year cardiovascular events from angiography.Nature cardiovascular research · 2026Article
- A Facial Foundation Model for Clinical Biomarker Prediction and Real-World Mobile Deployment.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- AI-augmented cardiopulmonary point-of-care ultrasound: a narrative review for pulmonary and critical care clinicians.Breathe (Sheffield, England) · 2026Article
- Artificial Intelligence in Pediatric Cardiovascular Genetics: From Multimodal Diagnosis to Risk Prediction and Precision Therapeutics.International journal of molecular sciences · 2026Review
- A Multitask Deep Learning Model for Pediatric Echocardiography Analysis.Circulation · 2026Article
- Artificial Intelligence in Cardiovascular Ultrasound: Clinical Applications, Foundation Models, and the Path to Precision Cardiology.Journal of clinical medicine · 2026Review
- Artificial Intelligence in Cardiac Surgery and Surgical Training: Opportunities, Risks, and Safeguards for Preserving Expertise.Journal of clinical medicine · 2026Review
- Special article-EchoPeer: a standardized framework for assessing echocardiography reports in the era of artificial intelligence: a recommendation from the Korean Society of Echocardiography AI and Future Strategy Committee.Journal of cardiovascular imaging · 2026Review
- Foundation models for cardiovascular diseases and medicine.European heart journal. Digital health · 2026Review
- Language-Guided Segmentation of Medical Images: A Review of Foundation Models.Bioengineering (Basel, Switzerland) · 2026Review
- Imaging-anchored multiomics in cardiovascular disease: integrating cardiac imaging, bulk, single-cell, and spatial transcriptomics.Briefings in bioinformatics · 2026Review
- Artificial Intelligence Across the Echocardiographic Workflow: A Narrative Review for Clinicians.Cureus · 2026Review
- Echocardiography-based intelligent diagnosis and risk stratification management for tetralogy of Fallot.EBioMedicine · 2026Article
- Multi-task artificial intelligence annotation of echocardiographic images: a retrospective multi-cohort study.medRxiv : the preprint server for health sciences · 2026Article
- FetalCLIP: a visual-language foundation model for fetal ultrasound image analysis.NPJ digital medicine · 2026Article
- Foundation model for screening severe mitral regurgitation and severe aortic stenosis from coronary angiograms.Visual computing for industry, biomedicine, and art · 2026Article
- An ultrasound foundation model for the stratification of vision impairment and eye cancer risk.NPJ digital medicine · 2026Article
46 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
Authors and funding
4 authors.
Funding
Abstract
The development of robust artificial intelligence models for echocardiography has been limited by the availability of annotated clinical data. Here, to address this challenge and improve the performance of cardiac imaging models, we developed EchoCLIP, a vision-language foundation model for echocardiography, that learns the relationship between cardiac ultrasound images and the interpretations of expert cardiologists across a wide range of patients and indications for imaging. After training on 1,032,975 cardiac ultrasound videos and corresponding expert text, EchoCLIP performs well on a diverse range of benchmarks for cardiac image interpretation, despite not having been explicitly trained for individual interpretation tasks. EchoCLIP can assess cardiac function (mean absolute error of 7.1% when predicting left ventricular ejection fraction in an external validation dataset) and identify implanted intracardiac devices (area under the curve (AUC) of 0.84, 0.92 and 0.97 for pacemakers, percutaneous mitral valve repair and artificial aortic valves, respectively). We also developed a long-context variant (EchoCLIP-R) using a custom tokenizer based on common echocardiography concepts. EchoCLIP-R accurately identified unique patients across multiple videos (AUC of 0.86), identified clinical transitions such as heart transplants (AUC of 0.79) and cardiac surgery (AUC 0.77) and enabled robust image-to-text search (mean cross-modal retrieval rank in the top 1% of candidate text reports). These capabilities represent a substantial step toward understanding and applying foundation models in cardiovascular imaging for preliminary interpretation of echocardiographic findings.
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.