ArticleBMJ open2024
Radiograph accelerated detection and identification of cancer in the lung (RADICAL): a mixed methods study to assess the clinical effectiveness and acceptability of Qure.ai artificial intelligence software to prioritise chest X-ray (CXR) interpretation.
Article in BMJ open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06044454 (RADICAL), which is not on this map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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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.
RADICAL: A Mixed Methods Study to Assess the Clinical Effectiveness and Acceptability of an Artificial Intelligence Software to Prioritise Chest X-ray (CXR) Interpretation
Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Concordance with SPIRIT-AI guidelines in reporting of randomized controlled trial protocols investigating artificial intelligence in oncology: a systematic review.The oncologist · 2025Pooled it
- AI-based chest X-ray prioritization in the lung cancer diagnostic pathway: the LungIMPACT randomized controlled trial.Nature medicine · 2026Trial
- Beyond Validation: Operationalising Post-Deployment Surveillance of AI Medical Devices in Clinical Practice.Journal of medical systems · 2026Article
- Patient and staff acceptability of an artificial intelligence software to prioritize chest X-rays in an NHS setting: a qualitative evaluation.BMC health services research · 2026Article
- Patient-centered AI.Frontiers in digital health · 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
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionDiagnosing and treating lung cancer in early stages is essential for survival outcomes. The chest X-ray (CXR) remains the primary screening tool to identify lung cancers in the UK; however, there is a shortfall of radiologists, while demand continues to increase. Image analysis by machine-learning software has the potential to support radiology workflows with a focus on immediate triage of suspicious X-rays. The RADICAL study will evaluate Qure.ai's 'qXR' software in reducing reporting time for suspicious X-rays in NHS Greater Glasgow & Clyde. METHODS AND ANALYSIS: This is a stepped-wedge cluster-randomised study consisting of a retrospective technical evaluation and prospective clinical effectiveness study alongside the assessment of acceptability via qualitative work and evaluation of cost-effectiveness via a cost utility analysis. The primary objective is to assess the clinical effectiveness of qXR to prioritise patients suspected with lung cancer on CXR for follow-up CT. Secondary objectives will look at the utility, safety, technical performance, health economics and acceptability of the intervention. The study period is 24 months, consisting of an initial 12 month data collection period and a 12 month follow-up period. All the standard care CXRs from outpatient and primary care requests will be securely transmitted to Qure.ai software 'qXR' for interpretation. Images with features of cancer will be flagged as 'Urgent Suspicion of Cancer' and be prioritised for radiologist review within the existing reporting workflow. ETHICS AND DISSEMINATION: The study will follow the principles of Good Clinical Practice. The protocol was granted REC approval in August 2023 from North West-Greater Manchester West Research Ethics Committee (REC 23/NW/0211). This study was registered on clinicaltrials.gov (NCT06044454). An interim report will be produced for use by the Scottish Government. The results from this study will be presented at artificial intelligence, radiology and respiratory meetings and published in peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT06044454.
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