Echocardiography remains the cornerstone of non-invasive cardiac imaging. For patients with suspected or known cardiac disease—including heart failure, cardiomyopathies, valvular heart disease, or ischemic heart disease—transthoracic echocardiography (TTE) enables assessment of cardiac structure, function, valves, and hemodynamic. However, TTE interpretation is often labor-intensive, operator-dependent, and subject to inter- and intra-observer variability. Recently, advancements in deep learning (DL) and artificial intelligence (AI) have yielded automated or semi-automated tools for echocardiographic tasks, such as view classification, chamber segmentation, quantification of volumes and ejection fraction, and even detection of pathological patterns. The latest generation of AI—namely vision-language models (VLMs) and foundation models trained on vast datasets of echocardiogram videos paired with clinical reports—promise to push boundaries further: from isolated measurements to comprehensive, report-level interpretation of entire TTE studies. In this narrative mini-review, we examine whether, in patients undergoing TTE, the use of large VLMs vs standard echocardiographer interpretation without AI can improve diagnostic accuracy, report quality, efficiency, reduce variability, and maintain safety. We appraise current evidence, highlight strengths and limitations, and discuss future perspectives and challenges.