Evidence map›Paper›PMID 41357329›Full record

ArticleEClinicalMedicine2025

Procurement and early deployment of artificial intelligence tools for chest diagnostics in NHS services in England: a rapid, mixed method evaluation.

Angus I G Ramsay, Nadia Crellin, Rachel Lawrence, Holly Walton, Stuti Bagri, Emma Dodsworth, Holly Elphinstone, Fergus Gleeson, Amanda Halliday, Kevin Herbert and 8 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in EClinicalMedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

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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

18 authors.

Angus I G RamsayInstitute of Epidemiology and Health Care, University College London, UK.
Nadia CrellinResearch and Policy, Nuffield Trust, London, UK.
Rachel LawrenceInstitute of Epidemiology and Health Care, University College London, UK.
Holly WaltonInstitute of Epidemiology and Health Care, University College London, UK.
Stuti BagriResearch and Policy, Nuffield Trust, London, UK.
Emma DodsworthResearch and Policy, Nuffield Trust, London, UK.
Holly ElphinstoneInstitute of Epidemiology and Health Care, University College London, UK.
Fergus GleesonDepartment of Oncology, University of Oxford, Oxford, UK.
Amanda HallidayPublic Contributor, Cambridgeshire, UK.
Kevin HerbertDepartment of Public Health and Primary Care, University of Cambridge, UK.
Joanne LloydPublic Contributor, Devon, UK.
Efthalia MassouDepartment of Public Health and Primary Care, University of Cambridge, UK.
Raj MehtaPublic Contributor, London, UK.
Stephen MorrisDepartment of Public Health and Primary Care, University of Cambridge, UK.
Pei Li NgInstitute of Epidemiology and Health Care, University College London, UK.
Tracy O'ReganThe Society and College of Radiographers, London, UK.
Chris Sherlaw-JohnsonResearch and Policy, Nuffield Trust, London, UK.
Naomi J FulopInstitute of Epidemiology and Health Care, University College London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Artificial Intelligence (AI) may support accurate, efficient radiology diagnostics. However, little is known about implementing AI in clinical settings. In 2023, NHS England launched a programme funding 12 networks of 66 NHS Trusts to implement AI for chest diagnostics, including lung cancer. Methods: Rapid evaluation (March-September 2024) of procurement and early deployment of AI for chest diagnostics at network (n = 10) and Trust (n = 6) levels. We interviewed network teams, Trust staff, and AI suppliers (n = 51); observed planning, governance, and training (n = 57); and analysed relevant documents (n = 166). The NASSS framework guided thematic analysis. Findings: Procurement and deployment of AI took longer than anticipated. Procurement involved engaging selection panels, assessing tenders, and contracting AI suppliers. Preparation for deployment involved AI integration; governance processes; staff engagement and training; planning patient engagement; and collating impact data; patient communication plans varied and were still developing. Challenges included: engaging staff with high clinical workloads; staff's limited AI knowledge, time to participate, and concerns over appropriate tool usage; managing unsuccessful suppliers' responses; and varied local governance processes, IT systems, and data availability and quality. Enablers included: programme leadership's support; networks sharing expertise and capacity; committed clinical, technical, and procurement specialists and AI suppliers; clinical champions; and dedicated project management. Interpretation: Implementing AI involved complex social and technical processes, requiring significant resources. Future implementation may benefit from ensuring sufficient time and capacity, ongoing stakeholder engagement at multiple levels, and greater consideration of patients and equity, diversity, and inclusion. Influential factors identified here mirror research on other healthcare innovations, suggesting AI may not address service challenges as straightforwardly as policymakers anticipate. Funding: National Institute for Health and Care Research (NIHR), Health and Social Care Delivery Research programme (NIHR156380). NJF and AIGR are supported by NIHR Central London Patient Safety Research Collaboration. NJF is an NIHR Senior Investigator.

Indexed as

Artificial intelligenceChest diagnosticsHealth services researchImplementationProcurementRadiology

Identifiers

PMID41357329
PMCPMC12675025

What OpenQuestion holds

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Registered trials

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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.