The field of medical image analysis is witnessing significant advancements with the integration of deep learning techniques, particularly in the detection of diseases from retinal images and breast cancer diagnosis. Researchers are exploring self-supervised learning approaches to alleviate the challenge of labeled data acquisition, demonstrating improved performance in downstream tasks. Moreover, hybrid models combining convolutional neural networks (CNNs) and transformer architectures are showing promise in enhancing diagnostic accuracy. In plant disease detection, the application of multimodal large language models and CNNs is yielding impressive results, with fine-tuned models achieving high classification accuracy. The use of dynamic dual-stream fusion networks and bidirectional knowledge distillation strategies is also enhancing recognition accuracy in plant disease recognition. Noteworthy papers include: DMS-Net, which achieves state-of-the-art performance in binocular fundus image classification, and VR-FuseNet, which proposes a hybrid deep learning model for diabetic retinopathy classification. Additionally, the survey on Vision Transformers in precision agriculture highlights their potential in transforming smart and precision agriculture.