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. 2025 May 12:83:103228.
doi: 10.1016/j.eclinm.2025.103228. eCollection 2025 May.

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

Rachel Lawrence  1 , Emma Dodsworth  2 , Efthalia Massou  3 , Chris Sherlaw-Johnson  2 , Angus I G Ramsay  1 , Holly Walton  1 , Tracy O'Regan  4 , Fergus Gleeson  5 , Nadia Crellin  2 , Kevin Herbert  3 , Pei Li Ng  1 , Holly Elphinstone  1 , Raj Mehta  6 , Joanne Lloyd  7 , Amanda Halliday  8 , Stephen Morris  3 , Naomi J Fulop  1
Affiliations

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

Rachel Lawrence et al. EClinicalMedicine. .

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 (N = 8013), resulting in 140 included studies. Studies evaluated implementation (N = 7), perceptions (N = 74), experiences (N = 14), effectiveness (N = 53), and cost (N = 6).

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.

Keywords: Artificial intelligence; Clinical practice; Diagnostics; Implementation; Radiology.

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Conflict of interest statement

AIGR is a trustee at Health Services Research UK. RM is Chair of the Board of Trustees of the Middlesex Association for the Blind; Vice-Chair on the Board of Trustees of the Research Institute for Disabled Consumers; Trustee on the Board of Thomas Pocklington Trust; Non-Executive Director on the Board of Evenbreak; Co-chair and Director on the Board of Shaping Our Lives. SM is currently (2022-) a member of the Small Business Research Initiative (SBRI) Healthcare panel. His post is funded in part by RAND Europe, a non-profit research organisation. SM is also Deputy Director of Applied Research Collaboration East of England (NIHR ARC EoE) at Cambridgeshire and Peterborough NHS Foundation Trust. NJF was a Non-Executive Director at Whittington Health NHS Trust until October 2024, a trustee at Health Services Research UK until 2022 and is a Non-Executive Director at Covid-19 Bereaved Families for Justice UK. TOR is part of the AXREM AI Special Focus Group, the British Institute of Radiology AI Special Interest Group, NHSE AI Deployment Fund Oversight Committee and Society of Radiographers AI Advisory Group. FG is a shareholder in Optellum Ltd, is a co-founder and Chairman of the RAIQC Ltd, was an advisor to NICE on the use of chest x-ray AI in the NHS and a committee member of the RCR Advisory group. All other authors report no declarations of interest.

Figures

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Fig. 1
Preferred reporting items for systematic reviews and meta-analyses (PRISMA) flow diagram.
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