Retinopathy of prematurity (ROP) is a major cause of vision loss and blindness among premature infants. Timely screening, diagnosis, and intervention can effectively prevent the deterioration of ROP. However, there are several challenges in ROP diagnosis globally, including high subjectivity, low screening efficiency, regional disparities in screening coverage, and severe shortage of pediatric ophthalmologists. The application of artificial intelligence (AI) as an assistive tool for diagnosis or an automated method for ROP diagnosis can improve the efficiency and objectivity of ROP diagnosis, expand screening coverage, and enable automated screening and quantified diagnostic results. In the global environment that emphasizes the development and application of medical imaging AI, developing more accurate diagnostic networks, exploring more effective AI-assisted diagnosis methods, and enhancing the interpretability of AI-assisted diagnosis, can accelerate the improvement of AI policies of ROP and the implementation of AI products, promoting the development of ROP diagnosis and treatment.