The field of medical imaging analysis is experiencing significant growth, driven by innovative advancements in generative modeling, lesion detection, and landmark detection. Recent developments have focused on creating more accurate and efficient methods for analyzing medical images, including the use of implicit neural representations, self-training pipelines, and large-scale labeled datasets. These advancements have the potential to improve diagnosis, treatment planning, and clinical workflows. Notably, researchers have made progress in addressing class imbalances and improving the generalizability of models. Furthermore, the introduction of large-scale datasets, such as the UK Biobank Organs and Bones, has enabled the development of more accurate and robust models. Noteworthy papers include the proposal of a steerable generative model based on implicit neural representations, which enables fine-grained control over shape variations. The introduction of the nnLandmark framework, a self-configuring method for 3D medical landmark detection, has also achieved state-of-the-art accuracy across two public datasets. Additionally, the UKBOB dataset has been presented, providing the largest labeled dataset of body organs and enabling zero-shot generalization of trained models on other small labeled datasets.