Observational studyBMJ open2021
Assessment of the effect of a comprehensive chest radiograph deep learning model on radiologist reports and patient outcomes: a real-world observational study.
Observational study in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05489471 (A Prospective Study to Assess the Impact of an Artificial Intelligence System on Reporting of Chest X-rays, Evaluate the Ability of AI Driven Worklists to Improve Reporting Times and Improve Same Day CT Pathway for Suspected Lung Cancer), which is not on this map. Cited by 19 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.
A Prospective Study to Assess the Impact of an Artificial Intelligence System on Reporting of Chest X-rays, Evaluate the Ability of AI Driven Worklists to Improve Reporting Times and Improve Same Day CT Pathway for Suspected Lung Cancer
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 46 citations in OpenAlex.
- Facilitators and Barriers to Implementing AI in Routine Medical Imaging: Systematic Review and Qualitative Analysis.Journal of medical Internet research · 2025Pooled it
- Impact of AI-Triaged Worklists and AI-Assisted Report Generation on Radiology Turnaround Times: Prospective Real-World Study.Journal of medical Internet research · 2026Article
- Prospective pilot evaluation of a deep learning model for kidney stone detection on CT using a web-based workflow platform.International urology and nephrology · 2026Article
- When the Diaphragm Deceives: Subpulmonic Effusion Flagged by AI and Confirmed by Ultrasound.Cureus · 2026Article
- Using generative artificial intelligence in clinical practice: a narrative review and proposed agenda for implementation.The Medical journal of Australia · 2025Review
- Artificial intelligence in medical imaging diagnosis: are we ready for its clinical implementation?Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Evaluation of the impact of artificial intelligence-assisted image interpretation on the diagnostic performance of clinicians in identifying endotracheal tube position on plain chest X-ray: a multi-case multi-reader study.Critical care (London, England) · 2025Article
- Efficiency and Quality of Generative AI-Assisted Radiograph Reporting.JAMA network open · 2025Article
- Artificial intelligence for diagnostics in radiology practice: a rapid systematic scoping review.EClinicalMedicine · 2025Article
- Effects of artificial intelligence implementation on efficiency in medical imaging-a systematic literature review and meta-analysis.NPJ digital medicine · 2024Review
- Quality of interaction between clinicians and artificial intelligence systems. A systematic review.Future healthcare journal · 2024Review
- Heterogeneity and predictors of the effects of AI assistance on radiologists.Nature medicine · 2024Article
- Commercially available artificial intelligence tools for fracture detection: the evidence.BJR open · 2024Review
- Pulmonary abnormality screening on chest x-rays from different machine specifications: a generalized AI-based image manipulation pipeline.European radiology experimental · 2023Article
- Incidental radiological findings during clinical tuberculosis screening in Lesotho and South Africa: a case series.Journal of medical case reports · 2023Article
- Artificial Intelligence-Based Software with CE Mark for Chest X-ray Interpretation: Opportunities and Challenges.Diagnostics (Basel, Switzerland) · 2023Review
- Impact of Different Artificial Intelligence User Interfaces on Lung Nodule and Mass Detection on Chest Radiographs.Radiology. Artificial intelligence · 2023Article
- Machine Learning Augmented Interpretation of Chest X-rays: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Frequency of Missed Findings on Chest Radiographs (CXRs) in an International, Multicenter Study: Application of AI to Reduce Missed Findings.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesArtificial intelligence (AI) algorithms have been developed to detect imaging features on chest X-ray (CXR) with a comprehensive AI model capable of detecting 124 CXR findings being recently developed. The aim of this study was to evaluate the real-world usefulness of the model as a diagnostic assistance device for radiologists.
designThis prospective real-world multicentre study involved a group of radiologists using the model in their daily reporting workflow to report consecutive CXRs and recording their feedback on level of agreement with the model findings and whether this significantly affected their reporting.
settingThe study took place at radiology clinics and hospitals within a large radiology network in Australia between November and December 2020.
participantsEleven consultant diagnostic radiologists of varying levels of experience participated in this study. PRIMARY AND SECONDARY OUTCOME MEASURES: Proportion of CXR cases where use of the AI model led to significant material changes to the radiologist report, to patient management, or to imaging recommendations. Additionally, level of agreement between radiologists and the model findings, and radiologist attitudes towards the model were assessed.
resultsOf 2972 cases reviewed with the model, 92 cases (3.1%) had significant report changes, 43 cases (1.4%) had changed patient management and 29 cases (1.0%) had further imaging recommendations. In terms of agreement with the model, 2569 cases showed complete agreement (86.5%). 390 (13%) cases had one or more findings rejected by the radiologist. There were 16 findings across 13 cases (0.5%) deemed to be missed by the model. Nine out of 10 radiologists felt their accuracy was improved with the model and were more positive towards AI poststudy.
conclusionsUse of an AI model in a real-world reporting environment significantly improved radiologist reporting and showed good agreement with radiologists, highlighting the potential for AI diagnostic support to improve clinical practice.
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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.