Age-related macular degeneration (AMD) represents a significant cause of visual impairment and blindness in individuals over 65 years old. In recent years, gene therapy has emerged as a research hotspot for wet AMD, with adeno-associated virus (AAV) vectors being widely utilized due to their non-pathogenic nature, low immunogenicity, broad tissue tropism, and capacity for sustained transgene expression. Several related studies have progressed to clinical trial stages. Although challenges persist, including immunogenicity concerns, limited vector capacity, and potential long-term adverse effects, the continuous advancement of research strategies and technologies holds promise. Future developments may employ AAV delivery systems to achieve gene supplementation, gene editing, or gene silencing of angiogenesis-related signaling molecules, thereby providing novel therapeutic approaches for wet AMD.
Diabetic retinopathy (DR) is a major cause of visual impairment among working-age populations. In recent years, artificial intelligence (AI) has demonstrated significant application value in DR diagnosis, leveraging core advantages such as high efficiency and low error rates. Currently, the technical system of AI in DR image diagnosis mainly includes links like image preprocessing, feature extraction, diverse algorithmic models, and dataset construction. In practical applications, AI models can achieve automated screening and grading diagnosis of DR images, enhance diagnostic efficiency by integrating multimodal technologies, and have been successfully applied to mobile devices; meanwhile, the development of explainable artificial intelligence has further boosted the credibility of AI models. Currently, this field still faces challenges, including insufficient data quality and scale, limited model interpretability, inadequate clinical validation, ethical and privacy risks, and a lack of unified technical standards. In the future, with continuous technological breakthroughs and the establishment of standardized evaluation systems, the reliability and accessibility of AI in DR diagnosis will be further enhanced.