Artificial intelligence for diagnostics in radiology practice: a rapid systematic scoping review
Open accessAbstract
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: = 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.
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A recent review found substantial gaps in real-world evaluation of implementation and use of AI for diagnostics in clinical settings, including no studies on procurement.
“A recent review notes substantial gaps in real-world evaluation of implementation and use of AI for diagnostics in clinical settings, for example, it found no studies on procurement.16”
From Procurement and early deployment of artificial intelligence tools for chest diagnostics in NHS services in England: a rapid, mixed method evaluation (2025) · cites this paper as [16]
Little is known about real-world implementation of AI in radiology diagnostics, including procurement, preparation for deployment, experiences of staff, patients, and carers, and impact on effectiveness and costs.
“However, little is known about real-world implementation, including procurement, preparation for deployment, experiences of staff, patients, and carers, and impact on effectiveness and costs—due in part to very few studies having been conducted on real-world implementation of AI tools for radiology diagnostics.16”
From Procurement and early deployment of artificial intelligence tools for chest diagnostics in NHS services in England: a rapid, mixed method evaluation (2025) · cites this paper as [16]
Little published evidence on real-world implementation and impact of AI on radiology diagnostics exists.
“However, little published evidence on real-world implementation and impact of AI on radiology diagnostics exists: this introduces risk into national and international deployment initiatives.”
From Procurement and early deployment of artificial intelligence tools for chest diagnostics in NHS services in England: a rapid, mixed method evaluation (2025) · cites this paper as [16]
The use of AI tools as decision support, rather than operating autonomously, aligned with existing UK regulation and public attitudes.
“The use of AI tools as decision support, rather than operating autonomously, aligned with existing UK regulation and public attitudes.16”
From Procurement and early deployment of artificial intelligence tools for chest diagnostics in NHS services in England: a rapid, mixed method evaluation (2025) · cites this paper as [16]
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- Authors
- Lawrence, R., Dodsworth, E., Massou, E., Sherlaw-Johnson, C., Ramsay, A. I. G., Walton, H., O'Regan, T., Gleeson, F., Crellin, N., Herbert, K., Ng, P. L., Elphinstone, H., Mehta, R., Lloyd, J., Halliday, A., Morris, S., & Fulop, N. J.
- Affiliations
- University College London (GB)
- Nuffield Trust (GB)
- University of Cambridge (GB)
- University of Oxford (GB)
- Corresponding author
- Lawrence, Rachel
- ORCID
- Venue
- eClinicalMedicine
- Publisher
- Elsevier BV
- Published
- 2025-05
- Volume
- 83
- Pages
- 103228
- ISSN
- 2589-5370 (ISSN-L)
- PMC
- PMC12140059
- OpenAlex
- W4410289560
- Cited by
- 69
- References
- 158
- Citations by year
- 2026: 56 · 2025: 13
- FWCI
- 16.61
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- EClinicalMedicine (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute for Health and Care Research (NIHR156380)
- Department of Health and Social Care
- Health and Social Care Delivery Research
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- artificial intelligence, radiology, diagnostic imaging, artificial intelligence adoption, diagnostic accuracy, false positives, workflow efficiency, cost-effectiveness, radiologist perception, implementation barriers, AI transparency, expert participation
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