Early Clinical Evaluation of AI Triage of Chest Radiographs: Time to Diagnosis for Suspected Cancer and Number of Urgent CT Referrals.

Authors

Mathew Storey, Jack Packer, Nora Sangvik Grandal, Anita Rhodes, Jo Moon, Daniel Togher, Ziyad Abubacker, Simon Rickaby, Geraldine Dean, Susan C Shelmerdine

Published in

NEJM AI. Volume 3. Issue 1. Pages AIcs2500539. Epub Dec 18, 2025.

Abstract

AbstractLung cancer is the leading cause of cancer mortality in the United Kingdom. Early diagnosis and rapid access to treatment are associated with better outcomes but are dependent on prompt recognition of findings and the availability of a computed tomography (CT) scanner and reporting. In this clinical evaluation case study, we report the use of artificial intelligence (AI) to triage suspected cancer on chest x-rays to a "radiographer-led same-day CT pathway." We included 56,257 chest radiographs acquired before and after real-world AI deployment across a National Health Service trust comprising five hospitals in South West London over a 3-year study period between January 2022 and December 2024 (1 year preintervention and 2 years postintervention). The time from chest radiograph acquisition to CT report for cases with suspected lung cancer was reduced from 6.0 to 3.6 days, with the most significant reductions at centers with colocated CT and x-ray facilities and no significant increase in the proportion of CTs performed relative to chest x-rays. In detecting suspected cancer, the 14-feature AI triage model has an 82.5% sensitivity, a 90.5% specificity, a 14.6% positive predictive value, and a 99.6% negative predictive value for possible lung cancer on chest x-rays. Features such as focal airspace opacity and single pulmonary nodule returned the highest proportion of false-positive results, while pulmonary and mediastinal masses had a greater diagnostic yield. This work demonstrates how AI triage can be implemented for the rapid onward diagnosis of lung cancer and informs the deployment of triage systems for urgent cancer pathways in general.

PMID:
42932526
Bibliographic data and abstract were imported from PubMed on 27 Oct 2026.

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