Artificial Intelligence in Cardiovascular Ultrasound: Clinical Applications, Foundation Models, and the Path to Precision Cardiology

Abstract

Cardiovascular ultrasound is a cornerstone of noninvasive cardiac and vascular assessment, yet conventional interpretation remains operator-dependent, variable, and limited in sensitivity for subclinical disease. Artificial intelligence (AI), particularly machine learning (ML), deep learning (DL), and, most recently, vision–language and foundation models, offers tools to automate, standardize, and extend ultrasound analysis. This narrative review examines the role of AI-enhanced cardiovascular ultrasound in the transition from descriptive imaging toward predictive and personalized medicine. We conducted a structured literature search of PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar (January 2016–May 2026), combining Medical Subject Headings and free-text terms related to AI and cardiovascular ultrasound. Original studies, meta-analyses, reviews, consensus documents, and seminal works were considered. AI now spans the entire echocardiographic workflow, acquisition guidance, view classification, segmentation (Dice ≈ 0.92–0.94 on public datasets), and automated quantification of ejection fraction and global longitudinal strain, achieving expert-level accuracy and improved reproducibility. Across clinical domains, AI supports ischemia detection on stress echocardiography, heart-failure phenogrouping, Doppler-independent aortic stenosis detection, and carotid plaque characterization for stroke-risk stratification. Emerging vision–language and multitask foundation models (e.g., EchoCLIP, EchoPrime, and PanEcho) point toward general-purpose interpretation, and a growing number of tools (Caption Guidance, Us2.ai, and Ultromics EchoGo) have obtained FDA clearance and/or CE marking. Increasingly, AI-derived imaging biomarkers feed multimodal models that enable individualized risk prediction and therapy selection. AI-enhanced cardiovascular ultrasound is poised to become a central tool of precision cardiology. Realizing its potential will require prospective multicenter validation, cross-vendor standardization, attention to generalizability, interpretability, and reproducibility, and evolving regulatory and ethical frameworks.