Intravitreal anti-vascular endothelial growth factor(anti-VEGF)therapy has been widely used, but the variability in its therapeutic efficacy limits individualized treatment. In recent years, the application of artificial intelligence(AI)has opened up new avenues for personalized treatment response prediction, and its core branches include machine learning(ML)and deep learning(DL). This review systematically retrieved and analyzed 41 relevant studies published up to April 2025. Comprehensive analysis reveals that AI predictive models are evolving from forecasting single endpoints(such as visual acuity or central retinal thickness)to integrating multi-dimensional endpoints(encompassing anatomical, functional, and treatment demand parameters)and generating predictive imaging outputs. In terms of technical approaches, DL models(28 studies, accounting for 68.3%)dominate this field due to their robust image interpretation capabilities, while ML models(10 studies, 24.4%)retain significant value in the analysis of structured clinical data. Cross-disease comparisons indicate that research efforts are most concentrated on age-related macular degeneration(ARMD)and diabetic macular edema(DME), with shared conceptual frameworks for model construction, yet distinct anatomical and functional indicators are prioritized for each disease. Currently, the field confronts several key challenges, including insufficient prospective clinical validation, limited model interpretability(the “black box problem”), and a scarcity of high-quality multi-center datasets. Moving forward, it is imperative to advance real-world validation and develop explainable AI techniques to expedite the clinical translation of these predictive models.

As the special subject of the applicant for registration of medical device, the research and development institutions have insufficient conditions and abilities to become medical device registrants, and there are certain difficulties in the actual registration application process, such as not clearing the certification path for the research and development institutions to hold the certificate. In view of the existing problems, by comparing the path of medicine research and development institutions to become medical device registrants and combining with the actual medical device industry to give relevant suggestions, including improving quality management over the whole life cycle of medical devices, quality and safety responsibility ability of research and development institutions, establishing the registration and certification path of research and development institutions, supporting laws and regulations, etc., so as to ensure that the research and development institutions become medical device registrants successfully.
Research ; Certification