Generative and Foundation-Based Artificial Intelligence in Medical Imaging: A Bibliometric Analysis of Global and United Kingdom Research, 2017-2025
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…
Read the paper
Related studies
- Learning from First-Generation Qualitative Approaches in the IS Discipline: An Evolutionary View and Some Implications for Authors and Evaluators (PART 1/2)
- Commentary on the clinical and preclinical dosage limits of interstitially administered magnetic fluids for therapeutic hyperthermia based on current practice and efficacy models
- Prosthesis: New Technological Opportunities and Innovative Biomedical Devices
- The history and future of neural modeling for cochlear implants
- Ryanodine receptors: waking up from refractoriness
- Novel Transgenic Rabbit Model Sheds Light on the Puzzling Role of Matrix Metalloproteinase-12 in Atherosclerosis
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.