New AI frameworks aimed at decoding complex biology and clinical workflows pushed into the spotlight, including a method to better predict hippocampal cognitive decline by layering polygenic risk with imaging signals. The approach, using models that integrate genetic risk into brain-imaging prediction, targets an area where early identification remains a major constraint. On the translational enablement side, researchers outlined a framework for clinical AI transparency by improving interpretability for predictions used in high-stakes medicine. The update addresses the “black box” concern by focusing on making outputs and decision factors more inspectable. Separately, an application in oncology imaging also emerged: computational tools reported for marker identification and monitoring via molecular and imaging strategies underscore how AI is increasingly being applied for precision stratification rather than just automation. Overall, the emphasis is shifting from model performance alone toward interpretability, multimodal data fusion, and integration into real decision pipelines.