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

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.

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.

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.

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.