ArticleLa Radiologia medica2026
AI assistance improves radiology resident reader performance in CT diagnosis of intracranial hemorrhage.
Article in La Radiologia medica, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Towards Explainability in Deep Learning for Detection of Five Major Intracranial Hemorrhage Subtypes on Head CT Using Multi-window DICOM Imaging and Patient-Level Cross-Validation.Journal of imaging informatics in medicine · 2026Article
- Human-AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions.Journal of imaging · 2026Review
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Authors and funding
15 authors.
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Abstract
purposeAccurate 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
methodsThis 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.
resultsAI 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.
conclusionBy 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.
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