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 of SHD. The approach is built on (1) a pretrained EHR foundation model (CLMBR-T) applied to 118 million coded events from 159,322 individuals to generate temporal patient embeddings and identify high-risk screening candidates, followed by (2) a novel contrastive vision-language model trained on 754,533 ECG image-echocardiogram report pairs to detect SHD subtypes with tunable performance characteristics. We evaluated this sequential, gated strategy in 5,198 individuals referred for their first transthoracic echocardiogram (TTE) within 90 days of an ECG (temporal validation), as well as in geographically distinct cohorts, including 33,518 UK Biobank participants undergoing protocolized ECG and cardiac magnetic resonance imaging, and a geographically distinct inpatient EHR cohort of 3,628 patients with ECG-TTE pairs (MIMIC-IV). Significance was determined by comparing metric differences between targeted and untargeted strategies, with bootstrap-derived 95% confidence intervals excluding zero considered significant. Results: Our pre-trained AI-ECG image foundation model discriminated 26 SHD subtypes, including left ventricular systolic dysfunction (AUROC of 0.90), severe aortic stenosis (AUROC of 0.85) and elevated right ventricular systolic pressure (AUROC of 0.82). Compared with untargeted AI-ECG screening, targeted screening in the temporal validation set (n=5,198) was associated with a significant increase in F1 scores (median of 0.25 [range: 0.09 to 0.75]) and decrease in false positives (median of -303 [range: -715 to -77]) across 26 SHD labels. Similar increases in F1 scores and reductions in false positives were seen in the UK Biobank (n=33,518; median change in false positives of -819 [range: -3,521 to -459] across 7 SHD labels) and MIMIC-IV (n=3,628; median false positive change of -255 [range: -716 to -86] across 5 SHD labels). Conclusions: TARGET-AI may guide the targeted deployment of AI-ECG for SHD screening by integrating longitudinal EHR phenotypes with multimodal ECG-echocardiogram representations in an interoperable framework, enabling adaptive, data-driven screening strategies across health systems.