The field of medical imaging and AI-assisted diagnosis is rapidly evolving, with a focus on developing innovative models and techniques to improve disease diagnosis and treatment. Recent studies have explored the use of machine learning and deep learning algorithms to analyze medical images and predict patient outcomes. Notably, researchers have made significant progress in developing models that can accurately predict postoperative neck pain in cervical spondylosis patients, detect fractures in X-rays, and classify musculoskeletal risk in athletes. Additionally, there have been advancements in the development of statistical shape models for anatomical analysis, such as the creation of a 3D stomach shape model. The use of AI-assisted diagnosis has also shown promise in reducing inter- and intra-reader variability in radiographic scoring, such as in the assessment of rheumatoid arthritis. Furthermore, researchers have proposed novel models for conditional cortical thickness forecasting, which can provide invaluable insights into neurodegenerative processes. Overall, these developments have the potential to significantly enhance clinical practice and improve patient outcomes. Noteworthy papers include: Pose as Clinical Prior, which introduces a novel framework for scoliosis screening using pose data, and TauGenNet, which proposes a text-guided 3D diffusion model for tau PET image synthesis. Veriserum is also notable for its contribution to the development of a dual-plane fluoroscopic dataset for deep learning in medical imaging.