The field of medical image segmentation is moving towards leveraging innovative data augmentation techniques and vision-language models to improve performance and reduce reliance on extensive expert annotations. Recent developments have focused on exploring the potential of hard and soft mixing data augmentation methods, as well as integrating vision-language models into semi-supervised learning frameworks. These approaches have shown promise in addressing data scarcity issues and improving segmentation accuracy. Notable papers include HSMix, which proposes a novel data augmentation approach involving hard and soft mixing for medical semantic segmentation, and ZEUS, which introduces a zero-shot visual-language segmentation pipeline for whole-slide images. Another noteworthy paper is VESSA, which integrates vision-language models into semi-supervised medical image segmentation, and SPROUT, which presents a fully training- and annotation-free prompting framework for nuclear instance segmentation.