ReviewDiagnostics (Basel, Switzerland)2023
Suboptimal Chest Radiography and Artificial Intelligence: The Problem and the Solution.
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 9 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
9 citing papers in PubMed.
- Bridging TG-116 and TG-232: A retrospective multi-vendor evaluation of deviation index re-centering and process control in digital radiography.Journal of applied clinical medical physics · 2026Article
- Artificial Intelligence in Pediatric Imaging: A Primer for Pediatric Clinicians.Indian journal of pediatrics · 2026Review
- From Modified Haller Index to a Novel Patented Anatomical Measurement Device: Engineering Development, Validation, and Clinical Applications of Non-Invasive Thoracic Morphometry.Bioengineering (Basel, Switzerland) · 2026Review
- Using Convolutional Neural Networks for the Classification of Suboptimal Chest Radiographs.Journal of medical radiation sciences · 2026Article
- Artificial Intelligence for Radiographic Image Quality: Radiographers at the Forefront.Journal of medical radiation sciences · 2026Article
- Evaluation of multimodal large language models for pneumothorax assessment in real-world clinical scenarios.BMC pulmonary medicine · 2026Article
- Assessing chest radiographic quality and the influence of COVID-19 pathology: the Australian experience.Journal of medical radiation sciences · 2025Article
- Investigating the Key Trends in Applying Artificial Intelligence to Health Technologies: A Scoping Review.PloS one · 2025Article
- National Diagnostic Reference Levels in General Radiography: A Scoping Review.Journal of medical physicsReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chest radiographs (CXR) are the most performed imaging tests and rank high among the radiographic exams with suboptimal quality and high rejection rates. Suboptimal CXRs can cause delays in patient care and pitfalls in radiographic interpretation, given their ubiquitous use in the diagnosis and management of acute and chronic ailments. Suboptimal CXRs can also compound and lead to high inter-radiologist variations in CXR interpretation. While advances in radiography with transitions to computerized and digital radiography have reduced the prevalence of suboptimal exams, the problem persists. Advances in machine learning and artificial intelligence (AI), particularly in the radiographic acquisition, triage, and interpretation of CXRs, could offer a plausible solution for suboptimal CXRs. We review the literature on suboptimal CXRs and the potential use of AI to help reduce the prevalence of suboptimal CXRs.
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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.