Generative Artificial Intelligence and Large Language Models as methodological support tools in health research: a bibliometric analysis

Abstract

Objective: To map the scientific output and thematic trends in the use of generative artificial intelligence (GenAI) and large language models (LLMs) as methodological support tools in health research.

Method: Bibliometric study conducted in September 2026 in the PubMed, Scopus, and Web of Science databases. Records were screened through a hybrid (human–AI) pipeline, in which an LLM applied predefined criteria and a human reviewer checked the results. Data were analyzed in RStudio using the bibliometrix package and its biblioshiny interface.

Results: The final corpus comprised 552 documents from 337 sources, with a mean document age of 0.88 years and an annual growth rate of 70.7%. The United States accounted for the largest number of author affiliations, followed by China. The literature showed high dispersion across journals and a strong concentration of occasional authors (89.2% with a single publication), along with modest international collaboration (14.7%). The thematic map revealed that motor themes remain centered on the generic use of LLMs and ChatGPT, whereas specific methodological applications have not yet formed consolidated clusters.

Conclusion: The use of GenAI and LLMs as methodological support in health research is an emerging and rapidly expanding field, but it still lacks a consolidated thematic structure and established publication venues. Consolidating the field will require normative validation criteria, transparent reporting, strict human oversight, and AI literacy among researchers, so as to safeguard the integrity of the scientific evidence produced, with direct implications for the quality of practice and the safety of health care.