ArticleBMJ open2024
AI assisted reader evaluation in acute CT head interpretation (AI-REACT): protocol for a multireader multicase study.
Article in BMJ open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06018545 (AI Assisted Reader Evaluation in Acute CT Head Interpretation), which is not on this map. Cited by 4 papers.
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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.
AI Assisted Reader Evaluation in Acute CT Head Interpretation
Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- Clinical Utility of AI-Enabled CT-to-MRI Translation for Degenerative Spinal Disorders: A Retrospective Reader Study.Diagnostics (Basel, Switzerland) · 2026Article
- Early diagnosis of axial spondyloarthritis in primary care using multi-agent systems.NPJ digital medicine · 2026Article
- Artificial intelligence-assisted reader evaluation in acute CT head interpretation (AI-REACT): a multireader multicase study.BMJ digital health & AI · 2026Article
- Evaluation of the impact of artificial intelligence-assisted image interpretation on the diagnostic performance of clinicians in identifying endotracheal tube position on plain chest X-ray: a multi-case multi-reader study.Critical care (London, England) · 2025Article
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Authors and funding
24 authors at 14 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionA non-contrast CT head scan (NCCTH) is the most common cross-sectional imaging investigation requested in the emergency department. Advances in computer vision have led to development of several artificial intelligence (AI) tools to detect abnormalities on NCCTH. These tools are intended to provide clinical decision support for clinicians, rather than stand-alone diagnostic devices. However, validation studies mostly compare AI performance against radiologists, and there is relative paucity of evidence on the impact of AI assistance on other healthcare staff who review NCCTH in their daily clinical practice. METHODS AND ANALYSIS: A retrospective data set of 150 NCCTH will be compiled, to include 60 control cases and 90 cases with intracranial haemorrhage, hypodensities suggestive of infarct, midline shift, mass effect or skull fracture. The intracranial haemorrhage cases will be subclassified into extradural, subdural, subarachnoid, intraparenchymal and intraventricular. 30 readers will be recruited across four National Health Service (NHS) trusts including 10 general radiologists, 15 emergency medicine clinicians and 5 CT radiographers of varying experience. Readers will interpret each scan first without, then with, the assistance of the qER EU 2.0 AI tool, with an intervening 2-week washout period. Using a panel of neuroradiologists as ground truth, the stand-alone performance of qER will be assessed, and its impact on the readers' performance will be analysed as change in accuracy (area under the curve), median review time per scan and self-reported diagnostic confidence. Subgroup analyses will be performed by reader professional group, reader seniority, pathological finding, and neuroradiologist-rated difficulty. ETHICS AND DISSEMINATION: The study has been approved by the UK Healthcare Research Authority (IRAS 310995, approved 13 December 2022). The use of anonymised retrospective NCCTH has been authorised by Oxford University Hospitals. The results will be presented at relevant conferences and published in a peer-reviewed journal. TRIAL REGISTRATION NUMBER: NCT06018545.
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