In recent years, the application of artificial intelligence (AI) in the healthcare domain has witnessed a significant surge, with deep learning emerging as a potent force in the medical field. Deep learning algorithms possess the remarkable ability to automatically extract intricate features and patterns, thereby facilitating highly accurate heart sound recognition. Drawing on this technological advancement, Professor Sun Kun and his research team from Xinhua Hospital, in collaboration with numerous centers spanning across China, have been diligently investigating the development and application of AI-assisted heart sound recognition for congenital heart disease (CHD) screening. Utilizing electronic stethoscopes to meticulously collect heart sounds, and harnessing AI algorithms to analyze extensive datasets comprising heart sounds from both children diagnosed with CHD and those who are healthy, the system has been trained to adeptly differentiate between normal and pathological murmurs. The current iteration of the system boasts an impressive accuracy and sensitivity rate of 90%. This study is designed as a randomized controlled trial (RCT) to be conducted at Shanghai Xinhua Hospital and Qinghai Provincial Women and Children's Hospital. The primary objective is to demonstrate the superiority of AI-assisted primary care physicians in identifying CHD over primary care physicians working independently. This will be achieved by conducting a comparative analysis of the performance of AI-assisted physicians versus their unassisted counterparts, thereby substantiating the model's practical applicability. Through an ongoing process of refinement and widespread application, this pioneering research endeavors to empower a diverse range of medical professionals, including general practitioners, child health physicians, and non-cardiovascular specialists, with the transformative capabilities of AI-assisted electronic auscultation. The ultimate goal is to elevate the standard of pediatric care across the nation.