Generative and Foundation-Based Artificial Intelligence in Medical Imaging: A Bibliometric Analysis of Global and United Kingdom Research, 2017-2025.
Authors
Affiliations (4)
Affiliations (4)
- Radiology, Lewisham and Greenwich NHS Trust, London, GBR.
- Surgery and Interventional Science, University College London, London, GBR.
- Obstetrics and Gynaecology, University College London Medical School, London, GBR.
- Radiology, Kettering General Hospital NHS Foundation Trust, Kettering, GBR.
Abstract
Artificial intelligence (AI), including generative and foundation-based methods, has rapidly expanded within medical imaging research, but the structure, citation impact, collaboration patterns, and thematic orientation of the United Kingdom national research ecosystem remain incompletely characterised. This bibliometric analysis examined Scopus-indexed publications from 2017 to 2025 using a predefined search strategy targeting generative and foundation-based AI methods in medical imaging. Records were analysed globally and filtered for United Kingdom affiliation. Descriptive indicators, including total publications, total citations, citations-per-paper, and year-on-year growth, were calculated. Co-authorship and keyword co-occurrence networks were generated using VOSviewer version 1.6.19. A total of 13,452 publications were identified globally, receiving 194,650 citations and a global citations-per-paper value of 14.47. Of these, 889 publications were United Kingdom-affiliated, representing 6.6% of global output. The United Kingdom ranked fourth by publication volume yet demonstrated higher unadjusted citations per paper than several higher-volume countries, with a value of 21.00. United Kingdom output increased approximately 18-fold between 2017 and 2025, with evidence of a citation-lag effect in recent years. The leading UK institutions had the highest full-count publication totals, although institution-level counts were not mutually exclusive because individual publications could be attributed to multiple organisations. Countries with more journal-dominant dissemination profiles also had higher unadjusted citations-per-paper values, although this descriptive comparison could not determine whether document-type composition explained the differences. Keyword analysis identified three principal thematic clusters: generative and deep learning methodologies, MRI- and diffusion-focused applications, and broader diagnostic imaging workflows. More recent highly cited publications included diffusion- and foundation-model architectures, although citation-age differences limit temporal interpretation. United Kingdom-affiliated research represents a rapidly expanding and highly cited component of the global generative and foundation-based AI in medical imaging literature. These findings provide a transparent bibliometric reference point for monitoring research activity, collaboration patterns, and potential translational priorities while recognising that citation-based indicators do not directly measure clinical implementation, methodological quality, or real-world impact.