ArticleDiagnostics (Basel, Switzerland)2022
Frequency of Missed Findings on Chest Radiographs (CXRs) in an International, Multicenter Study: Application of AI to Reduce Missed Findings.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Article
- Impact of AI Assistance in Pneumothorax Detection on Chest Radiographs Among Readers of Varying Experience.Diagnostics (Basel, Switzerland) · 2025Article
- Patient-centered AI.Frontiers in digital health · 2025Article
- Performance of AI to exclude normal chest radiographs to reduce radiologists' workload.European radiology · 2024Article
- Artificial intelligence-assisted double reading of chest radiographs to detect clinically relevant missed findings: a two-centre evaluation.European radiology · 2024Article
- X-ray interpretation in emergency department in the Kingdom of Saudi Arabia. Do we need the radiologist?Emergency radiology · 2024Article
- Early user perspectives on using computer-aided detection software for interpreting chest X-ray images to enhance access and quality of care for persons with tuberculosis.BMC global and public health · 2023Review
- Implementation of Artificial Intelligence-Assisted Chest X-ray Interpretation: It Is About Time.Annals of the American Thoracic Society · 2023Article
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14 authors.
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Abstract
backgroundMissed findings in chest X-ray interpretation are common and can have serious consequences.
methodsOur study included 2407 chest radiographs (CXRs) acquired at three Indian and five US sites. To identify CXRs reported as normal, we used a proprietary radiology report search engine based on natural language processing (mPower, Nuance). Two thoracic radiologists reviewed all CXRs and recorded the presence and clinical significance of abnormal findings on a 5-point scale (1-not important; 5-critical importance). All CXRs were processed with the AI model (Qure.ai) and outputs were recorded for the presence of findings. Data were analyzed to obtain area under the ROC curve (AUC).
resultsOf 410 CXRs (410/2407, 18.9%) with unreported/missed findings, 312 (312/410, 76.1%) findings were clinically important: pulmonary nodules (
conclusionA substantial number of important CXR findings are missed; the AI model can help to identify and reduce the frequency of important missed findings in a generalizable manner.
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