The field of medical imaging is moving towards developing more robust and generalizable models, particularly in the areas of domain generalization and artifact robustness. Researchers are exploring new approaches to improve the performance of deep learning models on unseen data, including the use of foundation models, anatomically-informed mixture-of-experts architectures, and domain adaptation techniques. These innovations have the potential to significantly improve the accuracy and reliability of medical imaging models in real-world clinical settings. Noteworthy papers include: Domain Generalization for Semantic Segmentation: A Survey, which provides a comprehensive overview of the current state of domain generalization research. REN: Anatomically-Informed Mixture-of-Experts for Interstitial Lung Disease Diagnosis, which introduces a novel framework for medical image classification that leverages anatomical priors to improve performance. Improving Artifact Robustness for CT Deep Learning Models Without Labeled Artifact Images via Domain Adaptation, which demonstrates the effectiveness of domain adaptation for improving the robustness of CT deep learning models to new artifacts. Adaptive Stain Normalization for Cross-Domain Medical Histology, which proposes a trainable color normalization model that can be integrated with any backbone network for downstream tasks.