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

Background: Non-contrast CT head scans (NCCTH) are the most frequently requested cross-sectional imaging in Emergency Departments. While AI tools exist to detect NCCTH abnormalities, evidence on their impact for non-radiologist clinicians is limited. Objective: To assess whether an AI tool improves the accuracy, speed, and confidence of general radiologists, emergency clinicians, and radiographers in detecting critical NCCTH abnormalities, and to evaluate its stand-alone performance and factors influencing diagnostic accuracy. Methods: A retrospective dataset of 150 NCCTH (52 normal, 98 with critical abnormalities) was reviewed by 30 readers (10 radiologists, 15 emergency clinicians, 5 radiographers) from four NHS trusts. Each interpreted scans unaided and then with the qER EU 2.0 AI tool, separated by a 2-week washout. Ground truth was established by two neuroradiologists. We measured the AI stand-alone performance and its effect on reader accuracy, confidence, and speed. Results: The qER algorithm showed strong diagnostic performance (AUC 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. 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.