Large language models in radiology: a bibliometric analysis of growth, collaboration, and thematic shifts (November 2022–July 2026)
Year: 2026
Type: article
Abstract: PURPOSE: To map the development of research on large language models (LLMs) in radiology from November 2022 to July 2026, focusing on publication trends, collaboration patterns, citation distribution, and thematic shifts among Science Citation Index Expanded (SCIE)-indexed original research articles. METHODS: A bibliometric analysis was conducted using records retrieved from the Web of Science Core Collection. Terms related to LLMs and radiology were combined at the topic (title, abstract, and keyword) level, and records retrieved by a complementary title-restricted strategy were screened jointly so that the analytic corpus reflected the union of both strategies. Only original research articles indexed in the SCIE were included. Records were screened independently by three reviewers (Fleiss' κ: 0.67). Publication trends, leading countries, institutions, authors, and journals; international collaboration networks; keyword co-occurrence patterns; citation distributions; and period-stratified thematic shifts were analyzed. Citation concentration was assessed with year-stratified sensitivity analyses, and a post hoc vision-language and foundation model sensitivity search quantified recent literature not captured by the primary strategy. RESULTS: . Keyword and period-stratified analyses showed a shift from early ChatGPT-centered feasibility studies toward general LLM and modality-specific evaluation, with keyword-level signals of an emerging multimodal direction. CONCLUSION: Research on LLMs in the SCIE-indexed radiology literature has expanded rapidly since 2022. Keyword patterns suggest a gradual shift from exploratory, text-only applications toward clinically oriented evaluation, with early keyword-level signals of a multimodal direction, although methodological heterogeneity and the absence of standardized, clinically meaningful evaluation frameworks remain the main barriers to clinical adoption. CLINICAL SIGNIFICANCE: By quantifying where, by whom, and on which themes LLM research in radiology is produced, this analysis helps radiologists, researchers, and funders identify under-studied modalities and applications, locate established collaboration networks, and direct future work toward the standardized, clinically meaningful evaluation frameworks the field currently lacks.
Institutions: Süleyman Demirel Üniversitesi, Akdeniz University
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7225771061
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