ArticleJournal of clinical medicine2020
Clinical Validation of a Deep Learning Algorithm for Detection of Pneumonia on Chest Radiographs in Emergency Department Patients with Acute Febrile Respiratory Illness.
Article in Journal of clinical medicine, 2020. 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 21 papers.
What it found
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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
21 citing papers in PubMed, 41 citations in OpenAlex.
- Comparison of Chest Radiograph Captions Based on Natural Language Processing vs Completed by Radiologists.JAMA network open · 2023Trial
- Multimodal AI-based 28-day mortality prediction of pneumonia patients at ED discharge: a multicenter study.Scientific reports · 2026Article
- Artificial Intelligence in Chest Radiography-A Comparative Review of Human and Veterinary Medicine.Veterinary sciences · 2025Review
- Application of Artificial Intelligence in Thoracic Radiology: A Narrative Review.Tuberculosis and respiratory diseases · 2025Article
- Deep learning for pediatric chest x-ray diagnosis: Repurposing a commercial tool developed for adults.PloS one · 2025Article
- Impact of human and artificial intelligence collaboration on workload reduction in medical image interpretation.NPJ digital medicine · 2024Article
- Navigating the Spectrum: Assessing the Concordance of ML-Based AI Findings with Radiology in Chest X-Rays in Clinical Settings.Healthcare (Basel, Switzerland) · 2024Article
- Deep learning improves physician accuracy in the comprehensive detection of abnormalities on chest X-rays.Scientific reports · 2024Article
- Effects of a comprehensive brain computed tomography deep learning model on radiologist detection accuracy.European radiology · 2024Article
- Machine Learning Augmented Interpretation of Chest X-rays: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Effects of Expert-Determined Reference Standards in Evaluating the Diagnostic Performance of a Deep Learning Model: A Malignant Lung Nodule Detection Task on Chest Radiographs.Korean journal of radiology · 2023Article
- Hospital-wide survey of clinical experience with artificial intelligence applied to daily chest radiographs.PloS one · 2023Article
- Machine Learning in the Classification of Pediatric Posterior Fossa Tumors: A Systematic Review.Cancers · 2022Review
- Localization-adjusted diagnostic performance and assistance effect of a computer-aided detection system for pneumothorax and consolidation.NPJ digital medicine · 2022Article
- Diagnostic performance of artificial intelligence approved for adults for the interpretation of pediatric chest radiographs.Scientific reports · 2022Article
- Artificial Intelligence-Based Detection of Pneumonia in Chest Radiographs.Diagnostics (Basel, Switzerland) · 2022Article
- Applications of artificial intelligence in the thorax: a narrative review focusing on thoracic radiology.Journal of thoracic disease · 2021Review
- The Added Effect of Artificial Intelligence on Physicians' Performance in Detecting Thoracic Pathologies on CT and Chest X-ray: A Systematic Review.Diagnostics (Basel, Switzerland) · 2021Review
- Effect of deep learning-based assistive technology use on chest radiograph interpretation by emergency department physicians: a prospective interventional simulation-based study.BMC medical informatics and decision making · 2021Article
- Chest radiographs and machine learning - Past, present and future.Journal of medical imaging and radiation oncology · 2021Review
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
Abstract
Early identification of pneumonia is essential in patients with acute febrile respiratory illness (FRI). We evaluated the performance and added value of a commercial deep learning (DL) algorithm in detecting pneumonia on chest radiographs (CRs) of patients visiting the emergency department (ED) with acute FRI. This single-centre, retrospective study included 377 consecutive patients who visited the ED and the resulting 387 CRs in August 2018-January 2019. The performance of a DL algorithm in detection of pneumonia on CRs was evaluated based on area under the receiver operating characteristics (AUROC) curves, sensitivity, specificity, negative predictive values (NPVs), and positive predictive values (PPVs). Three ED physicians independently reviewed CRs with observer performance test to detect pneumonia, which was re-evaluated with the algorithm eight weeks later. AUROC, sensitivity, and specificity measurements were compared between "DL algorithm" vs. "physicians-only" and between "physicians-only" vs. "physicians aided with the algorithm". Among 377 patients, 83 (22.0%) had pneumonia. AUROC, sensitivity, specificity, PPV, and NPV of the algorithm for detection of pneumonia on CRs were 0.861, 58.3%, 94.4%, 74.2%, and 89.1%, respectively. For the detection of 'visible pneumonia on CR' (60 CRs from 59 patients), AUROC, sensitivity, specificity, PPV, and NPV were 0.940, 81.7%, 94.4%, 74.2%, and 96.3%, respectively. In the observer performance test, the algorithm performed better than the physicians for pneumonia (AUROC, 0.861 vs. 0.788,
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Registered trials
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.