ReviewFrontiers in big data2023
AI-based radiodiagnosis using chest X-rays: A review.
Review in Frontiers in big data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 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.
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Who cites it
32 citing papers in PubMed, 59 citations in OpenAlex.
- Performance Comparison Between Two Versions of a Commercial Artificial Intelligence System for Chest Radiograph Interpretation: A Multicenter Study.Journal of imaging informatics in medicine · 2026Article
- Review
- A deep learning model for opportunistic screening of chronic kidney disease using chest radiographs: a multicentre validation study in the USA.EClinicalMedicine · 2026Article
- CCEO-DCABNet: Chronological Chaotic Evolution Optimization-Enabled Hybrid Deep Learning for Multiclass Disease Classification Using Chest X-Ray Images in Federated Learning.Diagnostics (Basel, Switzerland) · 2026Article
- WE-UNet: A Wavelet-enhanced U-Net framework for radiation dose reduction in chest radiography.Journal of applied clinical medical physics · 2026Article
- The evolving role of OMICS in gastrointestinal tumor biology and clinical practice.Molecular cancer · 2026Review
- Article
- Anatomy-Guided Radiology Report Generation With Pathology-Aware Regional Prompts.IEEE open journal of engineering in medicine and biology · 2026Article
- Transfer Learning for Automated Two-class Classification of Pulmonary Tuberculosis in Chest X-Ray Images.Current medicinal chemistry · 2026Article
- Risk assessment in cardiac surgery: Exploring machine learning and laboratory indices as adjunctive tools.PloS one · 2026Article
- Diagnostic Accuracy of an Offline CNN Framework Utilizing Multi-View Chest X-Rays for Screening 14 Co-Occurring Communicable and Non-Communicable Diseases.Diagnostics (Basel, Switzerland) · 2025Article
- CerviHFENet: hybrid feature extraction-based deep learning for multi-label classification of upper cervical spine abnormalities in X-ray imaging.BMC medical imaging · 2025Article
- Diagnostic accuracy of AI in chest radiography for pneumonia and lung cancer: A meta-analysis.European journal of radiology open · 2025Article
- Improving Performance, Robustness, and Fairness of Radiographic AI Models with Finely-Controllable Synthetic Data.Research square · 2025Article
- Evaluation of the effectiveness of the ChatGPT artificial intelligence application in the diagnosis of spontaneous pneumothorax on chest radiograph interpretation.BMC pulmonary medicine · 2025Article
- Artificial Intelligence-Powered Chronic Obstructive Pulmonary Disease Detection Techniques-A Review.Diagnostics (Basel, Switzerland) · 2025Review
- Silicodata: An Annotated Benchmark CXR Dataset for Silicosis Detection.Scientific data · 2025Article
- Scaling of Haralick features with image bit depth and gray level co-occurrence matrix displacement vector for linear gradients.Computers in biology and medicine · 2025Article
- Detection of pneumonia in children through chest radiographs using artificial intelligence in a low-resource setting: A pilot study.PLOS digital health · 2025Article
- Deep learning approach for automated hMPV classification.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chest Radiograph or Chest X-ray (CXR) is a common, fast, non-invasive, relatively cheap radiological examination method in medical sciences. CXRs can aid in diagnosing many lung ailments such as Pneumonia, Tuberculosis, Pneumoconiosis, COVID-19, and lung cancer. Apart from other radiological examinations, every year, 2 billion CXRs are performed worldwide. However, the availability of the workforce to handle this amount of workload in hospitals is cumbersome, particularly in developing and low-income nations. Recent advances in AI, particularly in computer vision, have drawn attention to solving challenging medical image analysis problems. Healthcare is one of the areas where AI/ML-based assistive screening/diagnostic aid can play a crucial part in social welfare. However, it faces multiple challenges, such as small sample space, data privacy, poor quality samples, adversarial attacks and most importantly, the model interpretability for reliability on machine intelligence. This paper provides a structured review of the CXR-based analysis for different tasks, lung diseases and, in particular, the challenges faced by AI/ML-based systems for diagnosis. Further, we provide an overview of existing datasets, evaluation metrics for different[][15mm][0mm]Q5 tasks and patents issued. We also present key challenges and open problems in this research domain.
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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.