Artificial intelligence-assisted reader evaluation in acute CT head interpretation (AI-REACT): a multireader multicase study

Journal article

Objective: To assess whether an artificial intelligence (AI) tool improves the accuracy, speed and confidence of general radiologists, emergency clinicians and radiographers in detecting critical non-contrast CT head (NCCTH) abnormalities and to evaluate its stand-alone performance and factors influencing diagnostic accuracy. Methods and analysis: A retrospective dataset of 150 NCCTH (52 normal and 98 with critical abnormalities) was reviewed by 30 readers (10 radiologists, 15 emergency clinicians and 5 radiographers) from four National Health Service trusts. Each interpreted scan is performed unaided and then with the qER EU 2.0 AI tool, separated by a 2-week washout period. Ground truth was established by two neuroradiologists. We measured the AI’s stand-alone performance and its effect on reader accuracy, confidence and speed. Results: The qER algorithm showed strong diagnostic performance (area under the receiver operator curve 0.821–0.976). With AI, pooled reader sensitivity for critical abnormalities increased from 82.8% to 89.7% (+6.9%, p<0.001) and for intracranial haemorrhage from 84.6% to 91.6% (+7.0%, p<0.001), while specificity decreased from 84.5% to 78.9% (–5.5%, p=0.046). Reader confidence did not change significantly. Emergency department (ED) clinicians with AI achieved sensitivity similar to unaided radiologists. Conclusion: AI assistance increased sensitivity for detecting critical abnormalities on NCCTH but reduced specificity. AI-enabled ED clinicians to achieve diagnostic sensitivity comparable to radiologists, supporting its potential to enhance non-radiologist performance. Further studies are needed to confirm these findings in clinical practice. Trial registration number: NCT06018545.

Authors: Novak, A; Shah, R; Espinosa Morgado, AT; Robert, D; Kumar, S

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: BMJ Publishing Group
• Journal: BMJ Digital Health & AI
• Volume: 2
• Issue: 1
• Pages: e000071
• Article number: bmjdh-2026-000071
• Publication date: 2026-03-12
• Acceptance date: 2026-02-03
• DOI: 10.1136/bmjdh-2026-000071
• EISSN: 3049-575X
• ISSN: 3049-575X
• Language: English
• Keywords: Artificial intelligence; Emergency Service, Hospital; Evidence-Based Emergency Medicine; Critical Care Outcomes