Artificial Intelligence Models for Eye Disease Diagnosis: A Systematic Review

ABSTRACT Early prediction and timely diagnosis of ocular diseases are of great significance for preventing vision loss and improving patients' quality of life. However, many eye diseases exhibit atypical symptoms in their early stages, leading to delays in clinical diagnosis and consequently postponing treatment opportunities, which may worsen the condition. Artificial intelligence (AI), particularly deep learning, has provided novel solutions for the automated detection and prediction of ophthalmic diseases. Methods such as convolutional neural networks (CNNs), transfer learning, generative adversarial networks (GANs), recurrent neural networks (RNNs), attention mechanisms, and interpretable deep learning have been widely applied in the analysis of fundus and optical coherence tomography (OCT) images. These approaches could extract subtle features that are difficult to capture using traditional techniques, achieving outstanding performance in the diagnosis and grading of diseases such as diabetic retinopathy, glaucoma, and macular degeneration. Therefore, a systematic analysis of the current applications and development trends of AI‐based methods in ocular disease prediction, along with a discussion of their advantages, limitations, and future improvement directions in alignment with clinical needs, will provide valuable insights for advancing intelligent diagnostic research and practice in retinal diseases.