Pediatric Hematology Oncology Journal

Soon, radiology residents will replace radiology AI models. Pedrini et al (2026) looked at 3 months' worth of CT scans (2,153) interpreted by an on-call resident. 15.4% of them had intracranial hemorrhage (ICH). The residents had a sensitivity of 96.4% and specificity of 99.6%. The same studies were given to directly to a commercial AI software, which generated heatmaps. The results and heatmaps were separately evaluated as part of the research protocol (aka not available at interpretation time) with sensitivity of 84% and specificity of 94.4%. Performance by AI improved with multiple hemorrhagic types or sites, but did not outperform the resident. Of 12 FN reports by residents, 6 would have been caught by the model; the attending that overread the preliminary report obviously found all 12. AI mislabeled 101 cases as FP for ICH. If these were autonomously read, this would have likely led to increased length of stay, follow-up imaging, or inappropriate treatment changes, including discontinuation or non-administration of thrombolytic therapy in patients with ischemia. The study authors note that the AI FP "typically would not cause confusion with ICH for radiologists interpreting CT scans, as they are easily recognized as various hyperdense intracranial abnormalities not related to ICH." Take-home points: 1) Clinical deployment studies are increasingly important for medical AI. The commercial model used here was good and well-validated. 2) Perhaps you should hire a Swiss radiology resident to read all your ICH cases, they seem pretty good. link.springer.com/article/10…