ReviewJournal of medical imaging and radiation oncology2021
Chest radiographs and machine learning - Past, present and future.
Review in Journal of medical imaging and radiation oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06075836 (Utility of an AI-based CXR Interpretation Tool in Assisting Diagnostic Accuracy, Speed, and Confidence of Healthcare Professionals), which is not on this map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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.
Utility of an AI-based CXR Interpretation Tool in Assisting Diagnostic Accuracy, Speed, and Confidence of Healthcare Professionals: a Study Using 500 Retrospectively Collected Inpatient and Emergency Department CXRs From Two UK Hospital Trusts
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Neural networks for classification of cervical vertebrae maturation: a systematic review.The Angle orthodontist · 2022Pooled it
- Diagnostic performance of an open-source artificial intelligence model for detecting chest infections on after-hours emergency department chest radiographs in Singapore.BMJ health & care informatics · 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
- Artificial intelligence for chest radiography: an overview of techniques, challenges, and future directions.npj health systems · 2026Review
- When the Diaphragm Deceives: Subpulmonic Effusion Flagged by AI and Confirmed by Ultrasound.Cureus · 2026Article
- Domain Shift Analysis in Chest Radiographs Classification in a Veterans Healthcare Administration Population.Journal of imaging informatics in medicine · 2026Article
- Recent advances in artificial intelligence for radiology report generation: a brief review.BJR artificial intelligence · 2026Review
- Designing a computer-assisted diagnosis system for cardiomegaly detection and radiology report generation.PLOS digital health · 2025Article
- Implementers Perspectives on the Routine Use of Artificial Intelligence in Health Services: A Qualitative Study Using the Consolidated Framework for Implementation Research (CFIR).Health services insights · 2025Article
- Quality Assessment of Plain Chest X-Rays of the Expatriates in Jordan: Multi-Centres Study.Risk management and healthcare policy · 2025Article
- Artificial intelligence-assisted chest radiograph interpretation in Role 2 military field hospital settings: a controlled experimental study.Trauma surgery & acute care open · 2025Article
- Classifying abnormalities in chest radiographs from Vietnam using deep learning for early detection of cardiopulmonary diseases.Frontiers in radiology · 2025Article
- AI-assisted detection for chest X-rays (AID-CXR): a multi-reader multi-case study protocol.BMJ open · 2024Article
- Improving prognostic accuracy in lung transplantation using unique features of isolated human lung radiographs.NPJ digital medicine · 2024Article
- Diagnostic modalities in the mediastinum and the role of bronchoscopy in mediastinal assessment: a narrative review.Mediastinum (Hong Kong, China) · 2024Review
- Analysis of Line and Tube Detection Performance of a Chest X-ray Deep Learning Model to Evaluate Hidden Stratification.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial Intelligence-Based Software with CE Mark for Chest X-ray Interpretation: Opportunities and Challenges.Diagnostics (Basel, Switzerland) · 2023Review
- Machine Learning Augmented Interpretation of Chest X-rays: A Systematic Review.Diagnostics (Basel, Switzerland) · 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite its simple acquisition technique, the chest X-ray remains the most common first-line imaging tool for chest assessment globally. Recent evidence for image analysis using modern machine learning points to possible improvements in both the efficiency and the accuracy of chest X-ray interpretation. While promising, these machine learning algorithms have not provided comprehensive assessment of findings in an image and do not account for clinical history or other relevant clinical information. However, the rapid evolution in technology and evidence base for its use suggests that the next generation of comprehensive, well-tested machine learning algorithms will be a revolution akin to early advances in X-ray technology. Current use cases, strengths, limitations and applications of chest X-ray machine learning systems are discussed.
Indexed as
Identifiers
What OpenQuestion holds
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.