The field of medical image analysis is witnessing significant advancements with the application of deep learning techniques. Recent developments focus on improving the accuracy and efficiency of disease diagnosis, such as diabetic retinopathy and skin cancer, using automated image classification systems. Researchers are exploring various deep learning frameworks, including TensorFlow, PyTorch, and JAX, to enhance the performance of these systems. Multimodal approaches, combining medical images with socioeconomic factors and comorbidity profiles, are also being investigated to improve diagnosis accuracy and address healthcare disparities. Furthermore, model compression and optimization techniques are being developed to enable the deployment of these models on resource-constrained devices, making them more accessible for use in underserved populations. Notable papers in this area include:

• One paper proposing a novel pipeline for diabetic retinopathy staging using retinal imaging and socioeconomic factors, which achieved high accuracy and addressed healthcare equity.
• Another paper presenting a robust deep learning framework for diabetic retinopathy classification using transfer learning and data augmentation, achieving state-of-the-art accuracy.
• A study proposing an AI-driven diagnostic tool for skin cancer detection, optimized for deployment on wearable devices, which maintained high performance while reducing model size and energy consumption.