ArticleNPJ digital medicine2025
Diagnosis of cardiac conditions from 12-lead electrocardiogram through natural language supervision.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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
5 citing papers in PubMed.
- Language-supervised electrocardiogram interpretation with vision transformer.Health information science and systems · 2026Article
- AI-Assisted Multilabel Diagnosis of 12-Lead Electrocardiograms Using an Interpretable Stacked Deep Learning Model with External Validation.Diagnostics (Basel, Switzerland) · 2026Article
- A multimodal deep learning model predicting hyperprogressive disease for PD-1 blockade in advanced hepatocellular carcinoma.NPJ digital medicine · 2026Article
- A Practical Approach to Assessing the Completeness of Electronic Health Records for Medical Research: Data Quality Study.JMIR medical informatics · 2026Article
- AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions.International journal of general medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Current AI approaches for cardiac diagnosis require condition-specific supervised learning with extensive labeled datasets, leading to fundamental scalability barriers. We developed an ECG-CLIP model, applying contrastive multimodal learning to enable zero-shot cardiac diagnosis from 12-lead ECGs using natural language supervision. Trained on 800,034 ECG-text pairs from MIMIC-IV-ECG, ECG-CLIP evaluated 18 cardiac conditions without condition-specific training. The model achieved superior performance for rhythm abnormalities (AUROC > 0.90) compared to morphological conditions. External validation demonstrated robust AUROC rank consistency (
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.