Evidence map›Paper›PMID 42254844›Full record

ReviewBJR artificial intelligence2026

Establishing a framework for development, prioritization, and assessment of artificial intelligence technology within the radiology department at a large UK teaching hospital.

James H R Cairns, Beverley Riley, Hanif Ismail, Bashar Al-Qaisieh, Mohua Siddique, Christopher Herbert, Bob Wheller, Fahmid U H Chowdhury, Andrew Scarsbrook

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

9 authors.

James H R CairnsFaculty of Medicine and Health, University of Leeds, Leeds LS2 9JT, United Kingdom.ORCID https://orcid.org/0009-0001-5942-3396
Beverley RileyLeeds Teaching Hospitals NHS Trust, Leeds, West Yorkshire, LS9 7TF, United Kingdom.ORCID https://orcid.org/0000-0002-3355-6430
Hanif IsmailLeeds Teaching Hospitals NHS Trust, Leeds, West Yorkshire, LS9 7TF, United Kingdom.
Bashar Al-QaisiehLeeds Teaching Hospitals NHS Trust, Leeds, West Yorkshire, LS9 7TF, United Kingdom.ORCID https://orcid.org/0000-0003-0745-784X
Mohua SiddiqueLeeds Teaching Hospitals NHS Trust, Leeds, West Yorkshire, LS9 7TF, United Kingdom.
Christopher HerbertLeeds Teaching Hospitals NHS Trust, Leeds, West Yorkshire, LS9 7TF, United Kingdom.
Bob WhellerLeeds Teaching Hospitals NHS Trust, Leeds, West Yorkshire, LS9 7TF, United Kingdom.ORCID https://orcid.org/0000-0003-3106-1722
Fahmid U H ChowdhuryLeeds Teaching Hospitals NHS Trust, Leeds, West Yorkshire, LS9 7TF, United Kingdom.ORCID https://orcid.org/0000-0002-1636-9086
Andrew ScarsbrookFaculty of Medicine and Health, University of Leeds, Leeds LS2 9JT, United Kingdom.ORCID https://orcid.org/0000-0002-4243-032X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) can revolutionize clinical workflows in radiology but requires organizational change. An institutional strategy to develop and evaluate AI tools is outlined. A multidisciplinary AI board with a patient and public involvement and engagement group was created. A comprehensive framework was formed, comprising workstreams covering information governance; technical rigor, performance, and safety; economic considerations; and ethical and medical-legal aspects. In addition to recurring meetings, a workshop with clinicians, information technology specialists, and patient representatives helped to identify priority use cases. Technical infrastructure was enhanced to support the development, performance assessment, and deployment of AI tools. Primary areas for AI applications included training staff, vetting of image requests, quality assurance, image interpretation, and communicating imaging findings to patients. Potential barriers, gaps in evidence, and subsequent actions for AI implementation were outlined. Avenues for collaboration with industry and market-available solutions were outlined. A virtual Picture Archiving and Communication System server was developed and then connected to a deployment platform for performance evaluation of AI products. Establishing an institutional AI board and imaging AI sandbox has guided safe, effective AI implementation while creating an ideal setting for innovation and industry partnership. Our approach to the integration of imaging AI provides a pragmatic guide for other institutions.

Indexed as

AIartificial intelligencecollaborationimplementationinnovationinstitutional approachmanagementorganizational approachpartnershipradiology

Identifiers

PMID42254844
PMCPMC13235739

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.