The recent advancements in the field of medical image analysis are significantly shifting towards more efficient and context-aware models. There is a notable trend of integrating state-space models, such as Mamba, into both segmentation and report generation tasks, leveraging their linear complexity and superior performance in long-context tasks. This approach aims to address the limitations of traditional deep learning models, particularly in handling large 3D medical volumes and generating detailed medical reports. The focus is on developing resource-efficient frameworks that maintain high precision and contextual understanding, thereby reducing the computational burden and enhancing clinical automation. Additionally, there is a growing emphasis on developing novel evaluation metrics that consider both textual and visual aspects of medical reports, ensuring a more comprehensive assessment of generated content. These developments collectively push the boundaries of what is achievable in medical image analysis, making significant strides towards more accurate and efficient healthcare solutions.