BMJ digital health & AI · 2026
Artificial intelligence-assisted reader evaluation in acute CT head interpretation (AI-REACT): a multireader multicase study.
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Novak Alex, Shah Ruchir, Espinosa Morgado Abdala T, Robert Dennis, Kumar Shamie, Oke Jason, Bhatia Kanika, Romsauerova Andrea, Das Tilak, AI-REACT Reader Study Group, Narbone Mariapaola, Dharmadhikari Rahul, Harrison Mark, Vimalesvaran Kavitha, Gooch Jane, Woznitza Nick, Lowe David, Shuaib Haris, Ather Sarim
Аннотация
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.
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.