ArticleDiagnostics (Basel, Switzerland)2022
Role of an Automated Deep Learning Algorithm for Reliable Screening of Abnormality in Chest Radiographs: A Prospective Multicenter Quality Improvement Study.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07744555 (Change in the Proportion of Correct Interpretations of Pulmonary Opacities by General Practitioners Using an Expert System), which is not on this map. Cited by 15 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.
Change in the Proportion of Correct Interpretations of Pulmonary Opacities by General Practitioners Using an Expert System
Who cites it
15 citing papers in PubMed, 23 citations in OpenAlex.
- From Algorithms to Clinics: Recent Progress in AI for Scoliosis Diagnosis and Management.Journal of clinical medicine · 2026Review
- Development of an AI model for pneumothorax imaging: Dataset and model optimization strategies for real-world deployment.European journal of radiology open · 2025Article
- Reducing the workload of medical diagnosis through artificial intelligence: A narrative review.Medicine · 2025Review
- A Systematic Review: The Role of Artificial Intelligence in Lung Cancer Screening in Detecting Lung Nodules on Chest X-Rays.Diagnostics (Basel, Switzerland) · 2025Review
- Classifying abnormalities in chest radiographs from Vietnam using deep learning for early detection of cardiopulmonary diseases.Frontiers in radiology · 2025Article
- Comparative Analysis of M4CXR, an LLM-Based Chest X-Ray Report Generation Model, and ChatGPT in Radiological Interpretation.Journal of clinical medicine · 2024Article
- Evaluation of the Performance of an Artificial Intelligence (AI) Algorithm in Detecting Thoracic Pathologies on Chest Radiographs.Diagnostics (Basel, Switzerland) · 2024Article
- Review
- Commercially available artificial intelligence tools for fracture detection: the evidence.BJR open · 2024Review
- Article
- Validation of a Deep Learning Chest X-ray Interpretation Model: Integrating Large-Scale AI and Large Language Models for Comparative Analysis with ChatGPT.Diagnostics (Basel, Switzerland) · 2023Article
- 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
- Using Artificial Intelligence to Stratify Normal versus Abnormal Chest X-rays: External Validation of a Deep Learning Algorithm at East Kent Hospitals University NHS Foundation Trust.Diagnostics (Basel, Switzerland) · 2023Article
- Pulmonary abnormality screening on chest x-rays from different machine specifications: a generalized AI-based image manipulation pipeline.European radiology experimental · 2023Article
- AI-Based CXR First Reading: Current Limitations to Ensure Practical Value.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In medical practice, chest X-rays are the most ubiquitous diagnostic imaging tests. However, the current workload in extensive health care facilities and lack of well-trained radiologists is a significant challenge in the patient care pathway. Therefore, an accurate, reliable, and fast computer-aided diagnosis (CAD) system capable of detecting abnormalities in chest X-rays is crucial in improving the radiological workflow. In this prospective multicenter quality-improvement study, we have evaluated whether artificial intelligence (AI) can be used as a chest X-ray screening tool in real clinical settings.
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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.