ArticleEuropean radiology2026
Performance of AI vs radiology residents in the detection of intracranial hemorrhage on emergency CT: a real-world analysis.
Article in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
- Commented on by
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo evaluate the performance of a commercial artificial intelligence (AI) software in detecting intracranial hemorrhage (ICH) in emergency settings, compared to on-call radiology residents. MATERIALS AND
methodsAll consecutive unenhanced cerebral CT-scans performed in a single center over a 3-month period in the emergency department in patients with suspected ICH, initially interpreted by radiology residents on-call and subsequently verified and approved by a board-certified radiologist, were concomitantly analyzed by an AI software for the presence of ICH. Results from the AI software were stored in a separate PACS partition and were unavailable to the radiologists for the case reading. We assessed the diagnostic performance of the AI software and of the radiology residents in detecting ICH. The reference standard was the final report of the board-certified radiologist.
resultsRadiology reports of 2153 CT-scans were analyzed, and ICH prevalence was 15.4% (331/2153). The AI software achieved an overall sensitivity of 84% and a specificity of 94.4%, and radiology residents achieved a sensitivity of 96.4% and a specificity of 99.6%, respectively (p-values < 0.001). The sensitivity was 97.7% for AI and 98.5% for residents when CT examinations displayed an association of multiple hemorrhagic types (p = 1). The sensitivity was 95.2% for AI and 98.4% for radiology residents in the presence of multiple ICH sites (p = 0.11).
conclusionRadiology residents demonstrated a significantly higher performance in detecting ICH compared to the AI software. AI exhibited very good diagnostic performance only in the presence of multiple hemorrhagic sites or multiple hemorrhage types. KEY POINTS: QuestionHow does the performance of the AI software compare to that of radiology residents in detecting ICH on unenhanced CT in real-life emergency workflow conditions? FindingsIn the emergency setting, the AI software demonstrated lower overall sensitivity and specificity than radiology residents for detecting ICH. Clinical relevanceIn real-life emergency conditions at a university hospital, the AI software did not offer a superior performance compared to radiology residents in detecting ICH. The integration of AI in this specific setting remains to be defined.
Indexed as
Identifiers
41721849What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.