A digital histopathology AI model achieved 100% sensitivity and negative predictive value in detecting bladder cancer lymph node metastases while reducing slide review time for pathologists. In validation at Sahlgrenska University Hospital, the system correctly identified every whole-slide image harboring metastatic disease. When deployed with model-generated overlays during review, median evaluation time dropped from 9 seconds to 4.2 seconds for one pathologist and from 12 seconds to 4 seconds for another. The report frames the approach as a workflow accelerant rather than a standalone replacement for clinical decision-making. For pathology and digital health teams, the key operational detail is the time savings achieved without compromising detection performance—an adoption hurdle that often determines real-world impact.