AI assistance improves radiology resident reader performance in CT diagnosis of intracranial hemorrhage

Purpose: Accurate detection of intracranial hemorrhage (ICH) on non-contrast CT is critical in emergency settings, where missed diagnoses may delay treatment and worsen outcomes. While artificial intelligence (AI) models demonstrate high standalone performance, their additive value as a second reader for radiology residents is not well-established. Material and methods: This retrospective study included 1,337 non-contrast head CT scans from 2015 to 2019 (670 ICH-positive and 667 ICH-negative). A previously validated AI model was used for ICH detection. Two radiology residents reviewed all scans in consensus, first without and later with AI support after a 30-day washout. Ground truth was established by expert consensus. Diagnostic performance metrics were calculated. Results: AI assistance significantly improved radiology residents’ diagnostic performance. Sensitivity increased from 0.85 to 0.94 and specificity from 0.87 to 0.97 (both p < 0.01), ROC-AUC rose from 0.86 to 0.95, and PR-AUC from 0.83 to 0.95 (p < 0.0001). The number of false negatives dropped from 101 to 41 with AI support. The greatest benefit was observed in subdural hematomas (SDH), where misses declined from 32 to 9 (20.3–5.7%; p < 0.001), corresponding to a 72% relative risk reduction. Misses also decreased for intraparenchymal hemorrhages (IPH: 37–20; RRR 46%) and subarachnoid hemorrhages (SAH: 30–11; RRR 63%). AI support reduced common error sources: small hemorrhage volume (48–21), atypical locations (30–12), and image-degrading artifacts (23–8). False positives fell from 87 to 21. Conclusion: By reducing diagnostic errors and supporting learning, AI serves as a valuable second reader for radiology residents—enhancing both patient safety and resident training in ICH detection.