ArticleEuropean radiology2022
Diagnostic effect of artificial intelligence solution for referable thoracic abnormalities on chest radiography: a multicenter respiratory outpatient diagnostic cohort study.
Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05489471 (A Prospective Study to Assess the Impact of an Artificial Intelligence System on Reporting of Chest X-rays, Evaluate the Ability of AI Driven Worklists to Improve Reporting Times and Improve Same Day CT Pathway for Suspected Lung Cancer), which is not on this map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Prospective Study to Assess the Impact of an Artificial Intelligence System on Reporting of Chest X-rays, Evaluate the Ability of AI Driven Worklists to Improve Reporting Times and Improve Same Day CT Pathway for Suspected Lung Cancer
Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- Deep learning for pneumothorax diagnosis: a systematic review and meta-analysis.European respiratory review : an official journal of the European Respiratory Society · 2023Pooled it
- Deep learning algorithm for automatic detection of acute ischemic stroke on noncontrast brain CT.Scientific reports · 2026Trial
- AI-based chest X-ray prioritization in the lung cancer diagnostic pathway: the LungIMPACT randomized controlled trial.Nature medicine · 2026Trial
- Impact of AI-Triaged Worklists and AI-Assisted Report Generation on Radiology Turnaround Times: Prospective Real-World Study.Journal of medical Internet research · 2026Article
- Diagnostic performance with sensitivity/specificity optimized computer-aided detection to detect common abnormalities in chest radiography.Japanese journal of radiology · 2026Article
- Artificial intelligence as a second reader for pneumothorax detection: redirecting rather than reducing chest CT use.BMC emergency medicine · 2026Article
- AI Solution for Identifying Actionable Abnormalities on Chest Radiographs: A Retrospective Case-Control Study.Journal of the Korean Society of Radiology · 2026Article
- Artificial intelligence (AI)-driven ensemble model for comprehensive chest X-ray abnormality detection and deployment.The Indian journal of medical research · 2026Article
- Impact of AI Assistance in Pneumothorax Detection on Chest Radiographs Among Readers of Varying Experience.Diagnostics (Basel, Switzerland) · 2025Article
- Evaluation of the impact of artificial intelligence-assisted image interpretation on the diagnostic performance of clinicians in identifying endotracheal tube position on plain chest X-ray: a multi-case multi-reader study.Critical care (London, England) · 2025Article
- Optimizing adult-oriented artificial intelligence for pediatric chest radiographs by adjusting operating points.Scientific reports · 2024Article
- Evaluation of the impact of artificial intelligence-assisted image interpretation on the diagnostic performance of clinicians in identifying pneumothoraces on plain chest X-ray: a multi-case multi-reader study.Emergency medicine journal : EMJ · 2024Article
- Factors for increasing positive predictive value of pneumothorax detection on chest radiographs using artificial intelligence.Scientific reports · 2024Article
- Evaluation of the Performance of an Artificial Intelligence (AI) Algorithm in Detecting Thoracic Pathologies on Chest Radiographs.Diagnostics (Basel, Switzerland) · 2024Article
- Clinical outcomes and actual consequence of lung nodules incidentally detected on chest radiographs by artificial intelligence.Scientific reports · 2023Article
- Pulmonary abnormality screening on chest x-rays from different machine specifications: a generalized AI-based image manipulation pipeline.European radiology experimental · 2023Article
- Novel Risks of Unfavorable Corticosteroid Response in Patients with Mild-to-Moderate COVID-19 Identified Using Artificial Intelligence-Assisted Analysis of Chest Radiographs.Journal of clinical medicine · 2023Article
- Clinical Implication and Prognostic Value of Artificial-Intelligence-Based Results of Chest Radiographs for Assessing Clinical Outcomes of COVID-19 Patients.Diagnostics (Basel, Switzerland) · 2023Article
- Use of Automated Machine Learning for Classifying Hemoperitoneum on Ultrasonographic Images of Morrison's Pouch: A Multicenter Retrospective Study.Journal of clinical medicine · 2023Article
- The impact of artificial intelligence on the reading times of radiologists for chest radiographs.NPJ digital medicine · 2023Article
Corrections and comments
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Authors and funding
9 authors at 5 institutions in 1 country.
Funding
Abstract
objectivesWe aim ed to evaluate a commercial artificial intelligence (AI) solution on a multicenter cohort of chest radiographs and to compare physicians' ability to detect and localize referable thoracic abnormalities with and without AI assistance.
methodsIn this retrospective diagnostic cohort study, we investigated 6,006 consecutive patients who underwent both chest radiography and CT. We evaluated a commercially available AI solution intended to facilitate the detection of three chest abnormalities (nodule/masses, consolidation, and pneumothorax) against a reference standard to measure its diagnostic performance. Moreover, twelve physicians, including thoracic radiologists, board-certified radiologists, radiology residents, and pulmonologists, assessed a dataset of 230 randomly sampled chest radiographic images. The images were reviewed twice per physician, with and without AI, with a 4-week washout period. We measured the impact of AI assistance on observer's AUC, sensitivity, specificity, and the area under the alternative free-response ROC (AUAFROC).
resultsIn the entire set (n = 6,006), the AI solution showed average sensitivity, specificity, and AUC of 0.885, 0.723, and 0.867, respectively. In the test dataset (n = 230), the average AUC and AUAFROC across observers significantly increased with AI assistance (from 0.861 to 0.886; p = 0.003 and from 0.797 to 0.822; p = 0.003, respectively).
conclusionsThe diagnostic performance of the AI solution was found to be acceptable for the images from respiratory outpatient clinics. The diagnostic performance of physicians marginally improved with the use of AI solutions. Further evaluation of AI assistance for chest radiographs using a prospective design is required to prove the efficacy of AI assistance. KEY POINTS: • AI assistance for chest radiographs marginally improved physicians' performance in detecting and localizing referable thoracic abnormalities on chest radiographs. • The detection or localization of referable thoracic abnormalities by pulmonologists and radiology residents improved with the use of AI assistance.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.