The field of ocular disease detection and treatment is rapidly advancing with the development of innovative artificial intelligence and deep learning-based systems. These systems aim to improve the accuracy and accessibility of diagnoses, particularly in underserved areas. Recent developments have focused on creating more efficient and scalable diagnostic tools, such as those utilizing convolutional neural networks and transformer-based models. Additionally, there is a growing interest in automating microsurgical tasks, such as retinal vein cannulation, to improve precision and reproducibility. Noteworthy papers in this area include EyeAI, which presents a system for AI-assisted ocular disease detection with a high degree of accuracy, and SwinECAT, which proposes a transformer-based model for fundus disease classification with state-of-the-art performance. HOG-CNN is also notable for its integration of handcrafted features with deep convolutional neural networks for retinal image classification, demonstrating high performance on multiple benchmark datasets.