Paper

TARGET-AI: A Foundational Approach for the Targeted Deployment of Artificial Intelligence Electrocardiography in the Electronic Health Record

Background Artificial intelligence (AI) applied to electrocardiograms (ECGs) offers promise for structural heart disease (SHD) screening, yet clinical integration remains challenged by high false-positive rates and a lack of tailored deployment strategies. Methods We developed TARGET-AI, a multimodal approach that integrates longitudinal electronic health records (EHRs) with ECGs to define patient phenotypes and support targeted SHD screening opportunities. TARGET-AI uses an EHR foundation model applied to 118 million events from 159,322 individuals, to generate temporal patient-level embeddings and identify screening candidates, and a contrastive vision-language model trained on 754,533 ECG image–echocardiogram report pairs, to detect distinct SHD subtypes with tunable characteristics. We evaluated this in 5198 individuals referred for a transthoracic echocardiogram (TTE) within 90 days of an ECG (temporal validation) and in geographically distinct cohorts, including 33,518 UK Biobank participants undergoing protocolized ECG and cardiac magnetic resonance imaging, and an inpatient cohort of 3628 patients from the Medical Information Mart for Intensive Care (MIMIC)-IV database with ECG–TTE pairs. We compared discrimination metrics between targeted and untargeted strategies, with bootstrap-derived 95% confidence intervals excluding zero considered significant. Results The TARGET-AI ECG model discriminated 26 SHD subtypes, including left ventricular systolic dysfunction (area under the receiver operating curve [AUROC] of 0.90), severe aortic stenosis (AUROC of 0.85), and elevated right ventricular systolic pressure (AUROC of 0.82). Compared with untargeted screening, targeted screening in the temporal validation set (n=5198) was associated with a significant increase in F1 scores (median: 0.25; range: 0.09 to 0.75) and a decrease in the number of false positives (median: −303; range: −715 to −77) across 26 SHD labels. Similar results were seen in the UK Biobank (n=33,518; median change in false positives: −819 [range: −3521 to −459] across seven SHD labels) and MIMIC-IV (n=3628; median false-positive change: −255 [range: −716 to −86] across five SHD labels) participants. Conclusions TARGET-AI may guide the targeted deployment of AI-ECG for SHD screening across health systems. (Funded by the National Heart, Lung, and Blood Institute of the National Institutes of Health and others.)

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• Paper published in NEJM AI

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