We are pleased to highlight a new publication from our group member Dr. Philipp Reschke in La Radiologia Medica, showcasing the significant value of artificial intelligence as a second reader in emergency neuroradiology. In this retrospective analysis of 1,337 non-contrast head CT examinations, a validated deep-learning model was used to support radiology residents in the detection of intracranial hemorrhage. The study demonstrates substantial diagnostic benefits: sensitivity increased from 0.85 to 0.94 and specificity from 0.87 to 0.98 with AI support, accompanied by marked improvements in ROC-AUC (0.86 to 0.95) and PR-AUC (0.83 to 0.95). Most importantly, the number of false negatives dropped from 101 to 41, with the greatest reduction observed in subdural hematomas, where AI assistance led to a 72% relative risk reduction. These findings underline the potential of AI to enhance patient safety, reduce missed findings, and support resident education in high-pressure clinical environments.