Evidence map›Paper›PMID 42063602›Full record

ReviewBJR artificial intelligence2026

Systematic prioritisation of AI-detected chest X-ray abnormalities for optimised lung cancer detection.

Rhidian R Bramley, Anna C Sharman, Rebecca Duerden, Sarah Lyon, Melissa Ryan, Elodie Weber, Louise Brown, Matthew Evison

Abstract readReview
In one paragraph

Review in BJR artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Rhidian R BramleyGreater Manchester Cancer Alliance, c/o The Christie NHS Foundation Trust, Manchester, M20 4BX, United Kingdom.ORCID https://orcid.org/0009-0005-0293-053X
Anna C SharmanDepartment of Radiology, Manchester University NHS Foundation Trust, Oxford Road, Manchester, M13 9WL, United Kingdom.ORCID https://orcid.org/0009-0003-1179-3210
Rebecca DuerdenDepartment of Radiology, Stockport NHS Foundation Trust, Stepping Hill Hospital, Poplar Grove, Stockport, SK2 7JE, United Kingdom.ORCID https://orcid.org/0009-0000-1711-5005
Sarah LyonGreater Manchester Cancer Alliance, c/o The Christie NHS Foundation Trust, Manchester, M20 4BX, United Kingdom.
Melissa RyanAnnalise.ai , 301/100 Harris Street, Pyrmont, NSW, 2009, Australia.ORCID https://orcid.org/0000-0003-3791-348X
Elodie WeberSectra Imaging IT Solutions, Teknikringen 20, SE-583 30, Linköping, Sweden.ORCID https://orcid.org/0009-0002-9839-0651
Louise BrownGreater Manchester Cancer Alliance, c/o The Christie NHS Foundation Trust, Manchester, M20 4BX, United Kingdom.ORCID https://orcid.org/0000-0003-0095-5029
Matthew EvisonLung Cancer & Thoracic Surgery Directorate, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester, M13 9WL, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper presents a reproducible, data-driven approach for prioritisation of AI-detected chest X-ray (CXR) findings to support faster lung cancer diagnosis in the NHS. The Annalise Enterprise CXR system was deployed in shadow mode across seven acute trusts in Greater Manchester. Two cohorts were used: a retrospective cancer cohort (

Indexed as

artificial intelligencechest X-raydiagnostic triagelung cancernegative predictive valueradiology workflowsensitivityshadow mode

Identifiers

PMID42063602
PMCPMC13070793

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.