The fields of ocular disease detection, computer vision, object detection, and wildlife conservation are experiencing rapid growth, driven by innovations in artificial intelligence and deep learning. A common theme among these areas is the development of more efficient and scalable diagnostic tools, such as those utilizing convolutional neural networks and transformer-based models. Notable advancements include the creation of AI-assisted ocular disease detection systems, such as EyeAI, and the proposal of transformer-based models for fundus disease classification, like SwinECAT. The integration of handcrafted features with deep convolutional neural networks, as seen in HOG-CNN, has also demonstrated high performance in retinal image classification. In computer vision, significant progress has been made in image identification, object recognition, and scene understanding, with the application of Vision Transformers and fuzzy logic showing potential in handling uncertainty and improving image analysis. The development of new frameworks and models, such as TrackAny3D and 3D-MOOD, has enhanced performance and generalization in 3D point cloud tracking, endoscopic depth estimation, and monocular 3D object detection. Object detection is moving towards more accurate and efficient detection of small targets in various environments, with improvements to existing models like YOLO, incorporating advanced features like attention mechanisms and self-supervised learning. Notable papers include Underwater Waste Detection Using Deep Learning, ABCD, and An Improved YOLOv8 Approach for Small Target Detection of Rice Spikelet Flowering. The field of wildlife conservation is increasingly leveraging computer vision to monitor and track animal populations, with a focus on improving species identification, object detection, and image classification. Large-scale datasets, such as AnimalClue, and deep learning models, like those presented in Evaluating Deep Learning Models for African Wildlife Image Classification, are being developed to support these efforts. Overall, these advancements have the potential to significantly impact various applications, including robotics, AR/VR, medical imaging, and wildlife conservation, and demonstrate the rapid progress being made in these fields.