AI Triage of Normal Chest Radiographs: A Silent Trial and Failure Analysis.
Mathew Storey1,2, Anthony Chung1,2, Jack Packer1,3
1Southwest London AI Working Group, London, UK.
Radiology. Artificial Intelligence
|April 22, 2026
Summary
Artificial intelligence (AI) can help triage normal chest radiographs (CXRs), potentially reducing reporting delays. This AI model showed high sensitivity and a low miss rate for significant findings in a large UK study.
Area of Science:
- Radiology
- Artificial Intelligence
- Healthcare Informatics
Background:
- Chest radiography (CXR) is the most common imaging exam globally.
- Increasing demand leads to reporting delays in healthcare systems.
- Efficient triage of CXRs is crucial for timely diagnosis.
Purpose of the Study:
- To evaluate the diagnostic performance of an AI model for triaging normal CXRs.
- To assess the AI model's ability to identify clinically significant abnormalities.
- To determine the potential of AI in prioritizing CXR reporting workflows.
Main Methods:
- Prospective multicenter silent trial involving 63,083 adult CXRs across five UK NHS sites.
- Evaluation of a commercial AI model (Annalise CXR Enterprise v2.38) for CXR triage.
- Expert review of AI-discrepant cases to determine clinically significant misses.
Main Results:
- AI model classified 80% of CXRs as abnormal and 20% as normal.
- Achieved 97% sensitivity and 94% negative predictive value for detecting abnormal CXRs.
- An estimated 0.05% clinically significant miss rate, with most misses involving subtle findings.
Conclusions:
- AI-assisted triage can help prioritize CXR reporting, potentially deprioritizing 18.5% of normal examinations.
- The AI model demonstrated a low rate of clinically significant missed findings.
- Further research is needed to evaluate the clinical implementation of AI for CXR triage.
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