The field of medical risk prediction and imaging analysis is moving towards a greater emphasis on incorporating temporal context and longitudinal data to improve accuracy and patient outcomes. Researchers are developing innovative machine learning frameworks and architectures that can effectively integrate diverse context from prior visits, imaging examinations, and clinical biomarkers to enhance health monitoring and disease prediction. These advancements have the potential to reduce false positive rates, improve specificity, and enable earlier detection and intervention for various progressive conditions. Notable papers in this area include: DuetMatch, which proposes a novel dual-branch semi-supervised framework for brain MRI segmentation, and MambaX-Net, which introduces a semi-supervised dual-scan 3D segmentation architecture for longitudinal prostate cancer analysis. Additionally, papers such as Temporally Detailed Hypergraph Neural ODEs and Time-Aware Δt-Mamba3D demonstrate the effectiveness of incorporating temporal context and irregular-time event samples in disease progression modeling and breast cancer risk prediction.