Evidence map›Paper›PMID 40474995›Full record

ArticleEClinicalMedicine2025

Artificial intelligence for diagnostics in radiology practice: a rapid systematic scoping review.

Rachel Lawrence, Emma Dodsworth, Efthalia Massou, Chris Sherlaw-Johnson, Angus I G Ramsay, Holly Walton, Tracy O'Regan, Fergus Gleeson, Nadia Crellin, Kevin Herbert and 7 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 27 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 1 pooled it
–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

27 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  5. Review
  6. Review
  7. Article
  8. Article
  9. Article
  10. An Exploration of Machine Learning Methods in Human Biomonitoring.International journal of environmental research and public health · 2026
    Review
  11. Review
  12. Review
  13. Article
  14. An Introduction to AI for Clinicians: Tutorial.Interactive journal of medical research · 2026
    Article
  15. Article
  16. Review
  17. Article
  18. Article
  19. Article
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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

17 authors.

Rachel LawrenceDepartment of Behavioural Science and Health, Institute of Epidemiology and Health Care, University College London, UK.
Emma DodsworthResearch and Policy, Nuffield Trust, London, UK.
Efthalia MassouDepartment of Public Health and Primary Care, University of Cambridge, UK.
Chris Sherlaw-JohnsonResearch and Policy, Nuffield Trust, London, UK.
Angus I G RamsayDepartment of Behavioural Science and Health, Institute of Epidemiology and Health Care, University College London, UK.
Holly WaltonDepartment of Behavioural Science and Health, Institute of Epidemiology and Health Care, University College London, UK.
Tracy O'ReganThe Society and College of Radiographers, London, UK.
Fergus GleesonDepartment of Oncology, University of Oxford, Oxford, UK.
Nadia CrellinResearch and Policy, Nuffield Trust, London, UK.
Kevin HerbertDepartment of Public Health and Primary Care, University of Cambridge, UK.
Pei Li NgDepartment of Behavioural Science and Health, Institute of Epidemiology and Health Care, University College London, UK.
Holly ElphinstoneDepartment of Behavioural Science and Health, Institute of Epidemiology and Health Care, University College London, UK.
Raj MehtaPublic Contributor, London, UK.
Joanne LloydPublic Contributor, Devon, UK.
Amanda HallidayPublic Contributor, Cambridgeshire, UK.
Stephen MorrisDepartment of Public Health and Primary Care, University of Cambridge, UK.
Naomi J FulopDepartment of Behavioural Science and Health, Institute of Epidemiology and Health Care, University College London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The aim of this review was to evaluate evidence on the use of Artificial Intelligence (AI) to support diagnostics in radiology, including implementation, experiences, perceptions, quantitative, and cost outcomes. Methods: We conducted a systematic scoping review (PROSPERO registration: CRD42024537518) and discussed emerging findings with relevant stakeholders (radiology staff, public members) using workshops. We searched four databases and the grey literature for articles published between 1st January 2020 and 31st January 2025. Articles were screened for eligibility ( Findings: Factors influencing AI adoption were identified, including the high technical demand, lack of guidance, training/knowledge, transparency, and expert engagement. Evidence demonstrated improvements in diagnostic accuracy and reductions in interpretation time. However, evidence was mixed regarding experiences of using AI, the risk of increasing false positives, and the wider impact of AI on workflow efficiency and cost-effectiveness. Interpretation: The potential benefits of AI are evident, but there is a paucity of evidence in real-world settings, supporting cautiousness in how AI is perceived (e.g., as a complementary tool, not a solution). We outline wider implications for policy and practice and summarise evidence gaps. Funding: This project is funded by the National Institute for Health and Care Research, Health and Social Care Delivery Research programme (Ref: NIHR156380). NJF and AIGR are supported by the National Institute for Health Research (NIHR) Central London Patient Safety Research Collaboration and NJF is an NIHR Senior Investigator. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.

Indexed as

Artificial intelligenceClinical practiceDiagnosticsImplementationRadiology

Identifiers

PMID40474995
PMCPMC12140059

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.