A validation study at Sahlgrenska University Hospital reported that a deep learning model can identify lymph node metastases in bladder cancer from digital histopathology while cutting pathologist review time. In a controlled test, the system detected all metastatic cases with sensitivity and negative predictive value reported at 100 percent. The workflow used model-generated visual overlays during slide reading, reducing median evaluation time for two experienced pathologists in the study. The result targets a specific operational bottleneck in bladder cancer staging—microscopic nodal assessment—where throughput and standardization remain recurring issues.