The field of medical imaging analysis is rapidly evolving, with a focus on developing innovative solutions to address the challenges of domain shift, data scarcity, and privacy concerns. Recent research has explored the use of domain-adaptive transformers, scale-aware curriculum learning, and privacy-aware continual self-supervised learning to improve the accuracy and robustness of medical image analysis models. Additionally, there is a growing interest in integrating anatomical priors into transformer architectures and adapting foundation models for medical image analysis. These advancements have the potential to enable more accurate and reliable diagnosis, as well as improved patient outcomes. Noteworthy papers include: PF-DAformer, which introduces a domain-adaptive transformer segmentation framework for multi-institutional QCT, and MedDChest, which proposes a new foundational Vision Transformer model optimized specifically for thoracic imaging. VisionCAD is also notable for its integration-free radiology copilot framework that captures medical images directly from displays using a camera system. Furthermore, MedSapiens demonstrates the potential of adapting human-centric foundation models for anatomical landmark detection in medical imaging.