Pancancer

Histopathology remains the gold standard for disease diagnosis, yet faces challenges including pathologist shortages and diagnostic model limitations. This underscores the critical need to develop deep learning-based pathology foundation models integrating prospective imaging and clinical data. Such models would enhance diagnostic accuracy and efficiency, enabling tumor grading, histo-molecular classification, and intelligent chemotherapy guidance - ultimately optimizing clinical workflows. However, a critical gap remains: the absence of prospectively validated, pan-disease pathology foundation models. Developing clinically validated models is therefore imperative.