Ran Heo, Seung-Ah Lee, Hyuk-Jae Chang
Echocardiography remains the cornerstone of cardiovascular imaging. However, traditional workflows including manual acquisition, sequential measurement, and expert interpretation face challenges from increased clinical demand, workforce shortage, and the physical burden of repetitive scanning. Artificial intelligence (AI) has begun to address these issues, transitioning from proof-of-concept to prospective clinical evaluations. Recent evidence suggests that AI integration reduces examination time and automates measurements, enabling more comprehensive data collection while mitigating sonographer fatigue and improving image quality. The sonographer's role is accordingly evolving from conventional measurement to active verification. AI applications in echocardiography now extend beyond ejection fraction to integrated assessments of myocardial texture and Doppler hemodynamics. New model architectures incorporate both structural and functional evaluations, reflecting clinical reasoning of the expert. These methods are being applied to valvular heart disease, cardiomyopathy, and pericardial disorders. Clinical implementation of AI in echocardiography requires more than high accuracy. Current evidence is limited by reliance on single-center studies, inconsistent performance across platforms, and the potential for automation bias in high-volume settings. This review evaluates current evidence, identifies existing gaps, and outlines the requirements for responsible clinical implementation of AI.