AI Triage of Normal Chest Radiographs: A Silent Trial and Failure Analysis

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 for triaging normal chest radiographs across five National Health Service hospital sites in the United Kingdom over a 12-month period. A total of 63 083 adult chest radiographs 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 chest radiographs. Expert review of discrepant cases, after exclusion of 412 natural language processing 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 chest radiographs, indicating that nearly one-fifth of examinations could potentially be deprioritized for reporting. These findings suggest that AI-assisted triage of chest radiographs may help prioritize reporting workflows while maintaining a low rate of clinically significant missed findings, although further research is warranted to evaluate clinical implementation. Keywords: Artificial Intelligence, Chest Radiography, Computer-aided Diagnosis, Thorax Supplemental material is available for this article. © RSNA, 2026.