The field of medical imaging analysis is rapidly evolving, with a focus on developing innovative models and techniques to improve disease diagnosis and treatment. Recent developments have seen a shift towards leveraging vision-language foundation models to reveal hidden image-attribute relationships and perform latent disentanglement for factorized medical image generation. This has led to significant advancements in areas such as chromosome abnormality detection, tissue detection, and coronary artery extraction. Notably, the integration of multimodal medical imaging and AI-based tissue detection has demonstrated superior performance in avoiding total failures on slides with unusual appearances. Furthermore, the use of diffusion models and image foundation models has improved correspondence matching in coronary angiography images, enabling more accurate 3D coronary artery structure reconstruction. While these developments hold great promise, there is a need for continued research into addressing biases in medical vision-language models and improving the representation of underrepresented groups in clinical datasets. Noteworthy papers include: iMedImage Technical Report, which proposes an end-to-end foundation model for medical image analysis, demonstrating superior performance across various medical imaging tasks. PathOrchestra, a comprehensive foundation model for computational pathology, which achieved exceptional performance across 112 clinical tasks and demonstrated the feasibility of a large-scale, self-supervised pathology foundation model. Leveraging Vision-Language Foundation Models, which investigates the question of whether fine-tuned foundation models can help identify critical, and possibly unknown, data properties, and demonstrates the potential of these models to reveal underlying dataset properties. Language-Guided Trajectory Traversal, which presents the first investigation of the power of pre-trained vision-language foundation models to perform latent disentanglement for factorized medical image generation and interpolation.