Artificial Intelligence Across the Echocardiographic Workflow: A Narrative Review for Clinicians.

Echocardiography is one of the most commonly used diagnostic methods in cardiovascular diseases because it is non-invasive, widely available, and capable of providing real-time assessment of function and cardiac structure. Despite these advantages, conventional echocardiography is often limited by operator dependence, interobserver inconstancy, the time-intensive nature of manual acquisition and measurement. Recent advances in artificial intelligence (AI), particularly deep learning, have created new opportunities to automate multiple steps of the echocardiographic workflow, starting from image acquisition and view classification to chamber segmentation, functional quantification, and hemodynamic estimation. This review provides an overview of the current clinical applications of artificial intelligence (AI) throughout the echocardiographic workflow. It focuses on key areas where AI has been applied, including automated view recognition, image quality assessment, cardiac phase identification, chamber segmentation, left ventricular ejection fraction estimation, strain analysis, and prediction of hemodynamic and disease-related parameters. Major challenges limiting wider clinical implementation were also highlighted in this review, such as insufficient external validation, dependence on image quality, differences between ultrasound vendors and patient populations, limited model interpretability, and lack of clear evidence demonstrating improved patient outcomes. Several AI-based tools, particularly those for automated view classification and chamber quantification, are becoming increasingly integrated into routine clinical practice, but many more advanced applications are possible, for which research is ongoing. The successful adoption of AI in echocardiography will depend not only on continued improvements in algorithm performance, but also on rigorous clinical validation, smooth integration into existing workflows, transparent reporting of model development and evaluation, and evidence that these technologies provide meaningful benefits for patient care.