Artificial Intelligence-Enhanced Echocardiography for Cardiac Tumor Detection: A Narrative Review of Advances, Challenges, and Clinical Translation

Introduction: Accurate detection and characterization of intracardiac masses remain a major challenge in cardiovascular imaging due to overlapping morphological features between tumors, thrombi, and vegetations, as well as the inherent limitations of echocardiography, including operator dependency and variable image quality. Although echocardiography is the first-line imaging modality for evaluating cardiac masses, diagnostic uncertainty frequently necessitates additional multimodality imaging. Artificial intelligence (AI), including machine learning and deep learning approaches, has emerged as a promising strategy to improve image interpretation, automate feature extraction, and enhance diagnostic consistency. Objective: This narrative review aims to examine current advances in AI-enhanced echocardiography for cardiac tumor detection, with a particular focus on detection, segmentation, classification, multimodal integration, and clinical translation. Methods: A narrative literature review was conducted using PubMed, Scopus, and Google Scholar databases. Relevant English-language studies published between 2016 and 2026 were identified using keywords including “artificial intelligence”, “machine learning”, “deep learning”, “echocardiography”, “cardiac tumors”, “intracardiac masses”, “multimodal imaging”, and “ultrasomics”. Original studies, reviews, and methodological papers related to AI-assisted cardiovascular imaging were evaluated. Discussion: Current evidence suggests that AI-driven techniques, including radiomics (ultrasomics), convolutional neural networks, and multimodal learning frameworks, can improve the detection, segmentation, and classification of intracardiac masses. Experimental studies have reported high diagnostic performance, with some deep learning models achieving diagnostic accuracies exceeding 95% under controlled conditions. AI-assisted systems may also reduce interobserver variability and improve workflow efficiency. Multimodal AI approaches integrating echocardiography with cardiac magnetic resonance imaging, computed tomography, electrocardiography, and clinical data appear particularly promising for improving diagnostic discrimination. However, current models remain limited by small and imbalanced datasets, insufficient external validation, data heterogeneity, and limited generalizability across institutions and imaging protocols. Additional barriers to clinical implementation include annotation variability, limited interpretability of deep learning models, and regulatory considerations. Conclusions: AI-enhanced echocardiography has substantial potential to improve the detection and characterization of intracardiac masses by augmenting diagnostic consistency and supporting clinical decision-making. Nevertheless, current evidence remains largely based on retrospective and experimental studies. Future progress will depend on large multicenter collaborations, standardized imaging datasets, explainable AI frameworks, and prospective clinical validation to enable safe and effective integration into routine cardiovascular practice.