The field of medical research is witnessing a significant shift towards the development of multimodal machine learning frameworks that integrate various data sources to improve diagnostic accuracy and clinical decision-making. Recent studies have demonstrated the potential of deep learning models in predicting treatment outcomes, classifying tumor response to therapy, and enhancing disease diagnosis and subtyping. Notable papers include a multi-modal machine learning framework for predicting early recurrence of brain tumors, a multimodal deep survival framework for prostate and bladder cancer, and a hybrid deep learning and radiomics approach for predicting brain tumor response to therapy. The integration of multimodal data has also shown promise in medical imaging, with the use of deep learning techniques improving the performance of image analysis tasks such as breast cancer detection and fetal plane classification. The incorporation of clinically-inspired approaches has further boosted the effectiveness of these models. Additionally, the development of innovative frameworks that seamlessly combine diverse data modalities has enhanced breast cancer subtyping and disease recognition. The field of dermatology is also witnessing a significant shift towards AI-assisted screening and diagnosis, with a focus on developing accessible and interpretable models for resource-limited environments. Recent studies have demonstrated the potential of deep learning models, particularly Transformer-based architectures, in classifying skin lesions and assessing skin health from mobile-acquired images. The field of hyperspectral image processing and analysis is rapidly evolving, with a focus on developing innovative methods for image denoising, defect detection, and classification. Recent research has explored the use of deep learning techniques, such as transformer-based architectures and U-Net models, to improve the accuracy and efficiency of these tasks. Overall, these advances have the potential to significantly impact various applications, including remote sensing, environmental monitoring, and precision agriculture. The development of hybrid models combining convolutional neural networks and transformers is also showing promising results in capturing global and local features, refining outputs, and eliminating redundant data. These developments have the potential to enhance computer-aided diagnosis and promote more equitable healthcare.