LAUSR

Recent advances in deep-learning technology have brought revolutionary changes to artificial intelligence (AI) research and application across industries, yielding major innovations such as facial recognition and self-driving cars. Medicine is no exception, and radiology, which is based on the interpretation of image data obtained through various methods-and has often been compared with computer vision using pattern analysis-is anticipated to experience a major revolution. Despite expectations for increasing research and development of AI-empowered ultrasonography, the clinical implementation of AI in medical ultrasonography faces unique obstacles. It will be necessary to standardize image acquisition, regulate operator and interpreter qualification and performance, integrate clinical information, and provide performance feedback to maximize benefits for patient care.