ArticleVisual computing for industry, biomedicine, and art2026
Mapping the generative era: a bibliometric and specialty-focused analysis of large language models in healthcare.
Article in Visual computing for industry, biomedicine, and art, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Large language models (LLMs) have been rapidly adopted in healthcare since 2022; however, field-level trends and specialty differences remain poorly characterized. This study aims to map the research landscape of LLMs in healthcare and generate comparative specialty profiles through a bibliometric analysis using the Web of Science Core Collection (2015-2025), including English-language articles and reviews analyzed for publication and citation trends, leading countries, institutions, authors, journals, co-citation networks, and keyword structures with VOSviewer and CiteSpace, with sub-analyses for medicine, general and internal; surgery; and radiology, nuclear medicine and medical imaging. Of the 2226 articles included, the annual output increased from four publications in 2022 to 1327 in 2025, yielding a compound annual growth rate (CAGR) of 592.26%; the United States of America contributed 43.5% of the publications, followed by China (14.3%), Germany (9.2%), Turkey (9.0%), and England (6.8%); the publications for each specialty were surgery (31.7%), medicine, general and internal (24.2%), and radiology, nuclear medicine and medical Iimaging (13.0%), with the last demonstrating the fastest 2023-2024 growth (CAGR 265%); and the collaboration networks were United States of America-centered, with dense trans-Atlantic ties. LLM research in healthcare is expanding rapidly with distinct specialty-specific trajectories, and interpreting these trajectories using the DECIDE-AI (developmental and exploratory clinical Iinvestigations of decision support systems driven by artificial intelligence) framework clarifies low-risk near-term applications and monitoring priorities, providing a specialty-aware baseline to support the safe, equitable, and regulation-aligned adoption of LLMs in clinical practice.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.