Artificial Intelligence in Retinal Disease Detection: Technological Advances and Clinical Applications Research

Retinal lesions, as common complications of chronic diseases such as diabetes and hypertension, have become a leading cause of irreversible visual impairment worldwide. According to the 2023 report by the International Diabetes Federation (IDF), approximately 34% of diabetic patients globally develop diabetic retinopathy (DR), with a subset progressing to vision-threatening advanced stages. Traditional screening methods depend on eye doctors to manually analyze fundus images, which can be difficult due to issues like low accuracy, uneven access to medical resources, and differences in opinions among doctors. In recent years, breakthroughs in deep learning have provided a novel methodological framework for medical image analysis. In particular, the innovative application of convolutional neural networks (CNNs) in retinal image interpretation has enabled a paradigm shift from reliance on manual expertise to data-driven approaches. This study adopts a systematic literature review to summarize the technological advances of artificial intelligence in retinal disease detection and discusses the associated clinical challenges and future prospects. The findings reveal that CNN-based models, such as ResNet-50, achieve high accuracy rates of up to 94.2% in DR grading, significantly outperforming manual screening. Additionally, generative adversarial networks (GANs) and multimodal fusion strategies effectively enhance performance in small-sample settings and improve detection sensitivity. However, issues such as data heterogeneity and limited model interpretability continue to hinder clinical application. It is therefore imperative to promote large-scale deployment of AI-assisted diagnostic systems through the construction of standardized multi-center databases, development of lightweight models, and design of human-computer collaborative diagnostic interfaces.