Global eye health has become a critical public health challenge, with the prevalence of blindness and visual impairment expected to rise significantly in the coming decades. Traditional ophthalmic public health systems face numerous obstacles, including the uneven distribution of medical resources, insufficient training for primary healthcare workers, and limited public awareness of eye health. Addressing these challenges requires urgent, innovative solutions. Artificial intelligence (AI) has demonstrated substantial potential in enhancing ophthalmic public health across various domains. AI offers significant improvements in ophthalmic data management, disease screening and monitoring, risk prediction and early warning systems, medical resource allocation, and health education and patient management. These advancements substantially improve the quality and efficiency of healthcare, particularly in preventing and treating prevalent eye conditions such as cataracts, diabetic retinopathy, glaucoma, and myopia. Additionally, telemedicine and mobile applications have expanded access to healthcare services and enhanced the capabilities of primary healthcare providers. However, there are challenges in integrating AI into ophthalmic public health. Key issues include interoperability with electronic health records (EHR), data security and privacy, data quality and bias, algorithm transparency, and ethical and regulatory frameworks. Heterogeneous data formats and the lack of standardized metadata hinder seamless integration, while privacy risks necessitate advanced techniques such as anonymization. Data biases, stemming from racial or geographic disparities, and the “black box” nature of AI models, limit reliability and clinical trust. Ethical issues, such as ensuring accountability for AI-driven decisions and balancing innovation with patient safety, further complicate implementation. The future of ophthalmic public health lies in overcoming these barriers to fully harness the potential of AI, ensuring that advancements in technology translate into tangible benefits for patients worldwide.

Background: Adeno-associated virus (AAV) plays a vital role in ocular gene therapy and has been widely studied since 1996. This study summarizes and explores the publication outputs and future research trends of AAV-based ocular gene therapy. Methods: Publications and data about AAV-based ocular gene therapy were downloaded from the Web of Science Core Collection or ClinicalTrials.gov database. The publications and data were analyzed by Microsoft Excel, CiteSpace, VOS viewer, and a free online platform (http://bibliometric.com). Results: Totally 832 publications from the Web of Science Core Collection relevant to AAV-based ocular gene therapy were published from 1996 to 2022. These publications were contributed by research institutes from 42 countries or regions. The US contributed the most publications among these countries or regions, notably the University of Florida. Hauswirth WW was the most productive author. “Efficacy” and “safety” are the main focus areas for future research according to the references and keywords analysis. Eighty clinical trials examined AAV-based ocular gene therapy were registered on ClinicalTrials.Gov. Institutes from the US and European did the dominant number or the large proportion of the trials. Conclusions: The research focus of the AAV-based ocular gene therapy has transitioned from the study in biological theory to clinical trialing. The AAV-based gene therapy is not limited to inherited retinal diseases but various ocular diseases.

Purpose: To investigate the impact of anti–vascular endothelial growth factor therapy on epiretinal macrophage-like cells (eMLC) in branch retinal vein occlusion with macular edema (BRVO-ME) patients. Methods: This retrospective study included 58 BRVO-ME patients treated with anti–vascular endothelial growth factor agents, divided into 2 groups: 28 eyes (1–2 Injection group) and 30 eyes (3 Injection group). The 3-injection group comprised 20 early responders and 10 limited early responders (LERs). Longitudinal changes in macular eMLC count and density were assessed through en face optical coherence tomography during the early response phase (baseline to 3 months postinitial injection). Linear regression and Spearman correlation analyzed eMLC density's relationship with optical coherence tomography angiography parameters and systemic inflammatory indices. Results: The 3-injection group exhibited greater reductions in macular edema and eMLC density than the 1 to 2 injection group. Macular eMLC density significantly decreased in early responders, whereas LERs showed no significant alteration. Posttreatment central macular thickness change positively correlated with eMLC density change (r = 0.57). Baseline systemic inflammatory indices (neutrophil/lymphocyte ratio, r = −0.46; neutrophil/platelet ratio, r = −0.36; systemic immune-inflammation index, r = −0.33; systemic inflammatory response index, r = −0.31) negatively correlated with posttreatment eMLC density change. Conclusion: Anti–vascular endothelial growth factor therapy reduces macular eMLC in BRVO-ME patients, especially in early responders receiving three injections. Macular eMLCs are closely associated with inflammation and may serve as a valuable treatment response biomarker.