ReviewDiagnostics (Basel, Switzerland)2023
Chest X-ray in Emergency Radiology: What Artificial Intelligence Applications Are Available?
Review in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- Blending electronic techniques with traditional teaching methods to enhance chest X-ray interpretation in medical students.PloS one · 2025Trial
- Chest radiography: an underappreciated source of litigation exposure in emergency radiology.Emergency radiology · 2026Article
- Default-threshold operating-point validation of a commercial chest radiograph AI system for selected CT-anchored thoracic findings: a bi-national multicenter retrospective study.Scientific reports · 2026Article
- Can clinicians quantify pulmonary congestion accurately using conventional modalities without remote dielectric sensing: ReDS quiz study.Heart and vessels · 2026Article
- AI Solution for Identifying Actionable Abnormalities on Chest Radiographs: A Retrospective Case-Control Study.Journal of the Korean Society of Radiology · 2026Article
- Fine-Tuned Segment Anything Model with Low-Rank Adaptation for Chest X-Ray Images.Diagnostics (Basel, Switzerland) · 2026Article
- Domain Shift Analysis in Chest Radiographs Classification in a Veterans Healthcare Administration Population.Journal of imaging informatics in medicine · 2026Article
- Artificial Intelligence in Pleural Diseases: Current Applications and Next Steps.Thoracic research and practice · 2026Article
- AI Revolution in Radiology, Radiation Oncology and Nuclear Medicine: Transforming and Innovating the Radiological Sciences.Journal of medical imaging and radiation oncology · 2025Review
- RADAI: A Deep Learning-Based Classification of Lung Abnormalities in Chest X-Rays.Diagnostics (Basel, Switzerland) · 2025Article
- Identifying Contextual Factors That Shape Cybersecurity Risk Perception for Assisted Living and Health Care Technologies and Wearables: Mixed Methods Study.Journal of medical Internet research · 2025Article
- ZooCNN: A Zero-Order Optimized Convolutional Neural Network for Pneumonia Classification Using Chest Radiographs.Journal of imaging · 2025Article
- An optimization method for hemi-diaphragm measurement of dynamic chest X-ray radiography during respiration based on graphics and diaphragm motion consistency criterion.Frontiers in physiology · 2025Article
- Post-deployment performance of a deep learning algorithm for normal and abnormal chest X-ray classification: A study at visa screening centers in the United Arab Emirates.European journal of radiology open · 2024Article
- Diagnostic Performance of Artificial Intelligence in Chest Radiographs Referred from the Emergency Department.Diagnostics (Basel, Switzerland) · 2024Article
- Review
- Factors for increasing positive predictive value of pneumothorax detection on chest radiographs using artificial intelligence.Scientific reports · 2024Article
- AI-Driven Thoracic X-ray Diagnostics: Transformative Transfer Learning for Clinical Validation in Pulmonary Radiography.Journal of personalized medicine · 2024Article
- AI-Based CXR First Reading: Current Limitations to Ensure Practical Value.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial Intelligence in Brain Tumor Imaging: A Step toward Personalized Medicine.Current oncology (Toronto, Ont.) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Due to its widespread availability, low cost, feasibility at the patient's bedside and accessibility even in low-resource settings, chest X-ray is one of the most requested examinations in radiology departments. Whilst it provides essential information on thoracic pathology, it can be difficult to interpret and is prone to diagnostic errors, particularly in the emergency setting. The increasing availability of large chest X-ray datasets has allowed the development of reliable Artificial Intelligence (AI) tools to help radiologists in everyday clinical practice. AI integration into the diagnostic workflow would benefit patients, radiologists, and healthcare systems in terms of improved and standardized reporting accuracy, quicker diagnosis, more efficient management, and appropriateness of the therapy. This review article aims to provide an overview of the applications of AI for chest X-rays in the emergency setting, emphasizing the detection and evaluation of pneumothorax, pneumonia, heart failure, and pleural effusion.
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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.