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.

ABSTRACT:

Introduction

Diagnosing 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 qualitativ

SUBMITTER: Duncan SF 

PROVIDER: S-EPMC11418533 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature