Chest radiography is the most frequently performed imaging examination worldwide, and increasing demand has contributed to reporting delays in many health systems. This prospective multicenter silent trial evaluated the diagnostic performance of a commercially available artificial intelligence (AI) model (Annalise CXR Enterprise v2.38) for triaging normal chest radiographs (CXRs) across five NHS hospital sites in the United Kingdom over a 12-month period. A total of 63,083 adult CXRs were analyzed. The AI model classified 50,661 examinations (80%) as abnormal and 12,422 (20%) as normal. The model achieved 97% sensitivity, 35% specificity, 57% positive predictive value, and 94% negative predictive value for detecting abnormal CXRs. Expert review of discrepant cases, after exclusion of 412 NLP labeling errors, identified 31 clinically significant AI misses, corresponding to an estimated clinically significant miss rate of 0.05%. Most missed findings involved subtle or overlapping lesions. Concordance between the AI model and radiologist reports for normal examinations occurred in 18.5% of CXRs, indicating that nearly one fifth of examinations could potentially be deprioritized for reporting. These findings suggest that AI-assisted triage of CXRs may help prioritize reporting workflows while maintaining a low rate of clinically significant missed findings, although further research is warranted to evaluate clinical implementation. ©RSNA, 2026.
Storey et al. (Wed,) studied this question.