Diagnosis of cardiac conditions from 12-lead electrocardiogram through natural language supervision.

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 ( ρ  = 0.934), including remarkable zero-shot performance for pediatric patients despite no pediatric training cases. Direct comparison showed ECG-CLIP ap

SUBMITTER: Zhou X 

PROVIDER: S-EPMC12630586 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature