The fields of neuroimaging, medical image understanding, and video analysis are experiencing rapid growth, driven by advancements in machine learning and deep learning techniques. A common theme among these areas is the development of innovative models and frameworks that can efficiently process and integrate multiple modalities of data, including images, videos, and text. In neuroimaging, recent studies have explored the use of deep learning techniques, such as weighted voting ensemble models and generative diffusion models, to enhance the accuracy of stroke diagnosis and crystal grain analysis. Noteworthy papers include the proposal of a concept-oriented synthetic data approach for training generative AI-driven crystal grain analysis, which achieved an average accuracy of 97.23%. Medical image understanding is also rapidly advancing, with the development of flexible and adaptable models that can learn from diverse data sources. The creation of unified frameworks for multimodal medical understanding and efficient vision-language models has enabled seamless integration of textual data with diverse visual modalities. Notable papers include Efficient Parameter Adaptation for Multi-Modal Medical Image Segmentation and Prognosis, and OmniV-Med: Scaling Medical Vision-Language Model for Universal Visual Understanding. The field of video analysis is moving towards more innovative and effective methods for recognizing and detecting actions in videos. Recent developments have focused on leveraging textual information to improve the accuracy and robustness of action localization models. Noteworthy papers include Chain-of-Thought Textual Reasoning for Few-shot Temporal Action Localization, and Grounding-MD, which presents a grounded video-language pre-training framework tailored for open-world moment detection. The development of models that can handle real-time video processing, such as streaming video understanding and online video interaction, is also a key trend. Improvements in video quality assessment and enhancement techniques, including the use of multimodal approaches, are additional areas of focus. Notable papers include ProVideLLM, which achieves state-of-the-art results on procedural video understanding tasks, and TimeChat-Online, which introduces a novel approach for real-time video interaction. Overall, these advancements have significant implications for disease diagnosis, treatment, and patient care, and highlight the importance of continued innovation in these fields.