Corneal neovascularization (CNV) is an ocular pathological change that results from an imbalance between angiogenic factors and antiangiogenic factors as a result of various ocular insults, including infection, inflammation, hypoxia, trauma, corneal degeneration, and corneal transplantation. Current clinical strategies for the treatment of CNV include pharmacological treatment and surgical intervention. Despite some degree of success, the current treatment strategies are restricted by limited efficacy, adverse effects, and a short duration of action. Recently, gene-based antiangiogenic therapy has become an emerging strategy that has attracted considerable interest. However, potential complications with the use of viral vectors, such as potential genotoxicity resulting from long-term expression and nonspecific targeting, cannot be ignored. The use of ocular nanosystems (ONS) based on nanotechnology has emerged as a great advantage in ocular disease treatment during the last two decades. The potential functions of ONS range from nanocarriers, which deliver drugs and genes to target sites in the eye, to therapeutic agents themselves. Various preclinical studies conducted to date have demonstrated promising results of the use of ONS in the treatment of CNV. In this review, we provide an overview of CNV and its current therapeutic strategies and summarize the properties and applications of various ONS related to the treatment of CNV reported to date. Our goal is to provide a comprehensive review of these considerable advances in ONS in the field of CNV therapy over the past two decades to fill the gaps in previous related reports. Finally, we discuss existing challenges and future perspectives of the use of ONS in CNV therapy, with the goal of providing a theoretical contribution to facilitate future practical growth in the area.

With the continuous development of computer technology, big data acquisition and imaging methods, the application of artificial intelligence (AI) in medical fields is expanding. The use of machine learning and deep learning in the diagnosis and treatment of ophthalmic diseases is becoming more widespread. As one of the main causes of visual impairment, myopia has a high global prevalence. Early screening or diagnosis of myopia, combined with other effective therapeutic interventions, is very important to maintain a patient's visual function and quality of life. Through the training of fundus photography, optical coherence tomography, and slit lamp images and through platforms provided by telemedicine, AI shows great application potential in the detection, diagnosis, progression prediction and treatment of myopia. In addition, AI models and wearable devices based on other forms of data also perform well in the behavioral intervention of myopia patients. Admittedly, there are still some challenges in the practical application of AI in myopia, such as the standardization of datasets; acceptance attitudes of users; and ethical, legal and regulatory issues. This paper reviews the clinical application status, potential challenges and future directions of AI in myopia and proposes that the establishment of an AI-integrated telemedicine platform will be a new direction for myopia management in the post-COVID-19 period.