The fields of predictive modeling for cancer treatment and neurodegenerative diseases, microstructure analysis and manufacturing, medical imaging analysis, medical computer vision, image analysis, image processing and generation, vision-language models, medical imaging diagnostics, image reconstruction and denoising, medical image analysis, medical image segmentation, remote sensing and image segmentation, and brain MRI synthesis and analysis are rapidly evolving. A common theme among these fields is the development of innovative frameworks that integrate multimodal data and leverage advanced machine learning techniques to improve patient outcomes and enhance treatment precision. Notable papers include Live(r) Die, which presents a fully automated framework for surgical outcome prediction in colorectal liver metastasis, and A Multimodal Foundation Model to Enhance Generalizability and Data Efficiency for Pan-cancer Prognosis Prediction, which introduces a novel multimodal foundation model for precise pan-cancer prognosis prediction. Researchers are also exploring new methods for meshing and remeshing microstructures, such as hierarchical diffusion-based approaches, to enhance the quality of discretization and preserve surface morphology. Additionally, there is a growing interest in symmetry-projection techniques to enforce expected symmetries in postprocessing and reduce errors in homogenization simulations. Innovative manufacturing methods, such as 3D Fiber Tethering, are being developed to create complex 3D architectures with continuous fiber reinforcement, enabling the production of lightweight and high-strength composite structures. The use of zero-shot learning algorithms, sparse mixture-of-experts methods, and recurrent neural networks has shown promise in predicting disease progression, treatment response, and patient survival. These advances have significant implications for personalized medicine, enabling clinicians to make more informed decisions and develop tailored treatment strategies. Furthermore, the development of new architectures and training methods has enabled significant advancements in image super-resolution, low-light image enhancement, and image editing. The integration of deep learning techniques and traditional methods has also led to improved reconstruction quality in single-pixel imaging tasks. Overall, these advancements have the potential to significantly improve clinical decision-making and patient outcomes.