TARGET-AI: a foundational approach for the targeted deployment of artificial intelligence electrocardiography in the electronic health record.

BACKGROUND

Artificial intelligence (AI) applied to routine electrocardiograms (ECGs) offers promise for screening of structural heart disease (SHD), yet broad clinical integration remains limited by high false positive rates and the lack of tailored deployment strategies.

METHODS

We developed TARGET-AI, a multimodal AI-enabled pipeline that integrates longitudinal electronic health record (EHR) data with ECG images to identify optimal intersections of healthcare encounters and patient phenotypes for targeted AI-ECG screening. The approach is built on (1) a foundation model pretrained on 118 million coded EHR events from 159,322 individuals to generate temporal patient embeddings and identify high-risk screening candidates, followed by (2) a contrastive vision-language model trained on 754,533 ECG-echocardiogram pairs to detect SHD with tunable performance characteristics. We evaluated this joint strategy in a temporally distinct cohort of 5,198 individuals referred for their first transthoracic echocardiogram (TTE) within 90 days of an ECG and externally in 33,518 participants from the UK Biobank undergoing ECG and cardiac magnetic resonance imaging.

RESULTS

Our pre-trained AI-ECG image foundation model discriminated 27 SHD subtypes, from left ventricular systolic dysfunction (AUROC of 0.90) to severe aortic stenosis (AUROC of 0.85) and elevated right ventricular systolic pressure (AUROC of 0.82). Compared with untargeted AI-ECG screening, EHR-informed TARGET-AI-guided screening significantly reduced false positive predictions across SHD labels (median reduction: 87.8%; interquartile range [IQR], 82.4%-98.2%) and improved F1 score (median increase: 0.25; IQR, 0.19-0.41). In the UK Biobank, targeted screening reduced false positives by 61.7% (IQR, 50.4%-89.1%) while preserving sensitivity.

CONCLUSIONS

TARGET-AI enables the context-aware deployment of AI-ECG screening by leveraging key longitudinal EHR phenotypes and multimodal ECG-echocardiogram representations, thereby defining an interoperable, data-driven strategy for the more precise deployment of AI screening tools across health systems.