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. 2025 Jan 17:18:223-238.
doi: 10.2147/JMDH.S502351. eCollection 2025.

Large Language Models in Healthcare: A Bibliometric Analysis and Examination of Research Trends

Gülcan Gencer  1 , Kerem Gencer  2
Affiliations

Large Language Models in Healthcare: A Bibliometric Analysis and Examination of Research Trends

Gülcan Gencer et al. J Multidiscip Healthc. .

Abstract

Background: The integration of large language models (LLMs) in healthcare has generated significant interest due to their potential to improve diagnostic accuracy, personalization of treatment, and patient care efficiency.

Objective: This study aims to conduct a comprehensive bibliometric analysis to identify current research trends, main themes and future directions regarding applications in the healthcare sector.

Methods: A systematic scan of publications until 08.05.2024 was carried out from an important database such as Web of Science.Using bibliometric tools such as VOSviewer and CiteSpace, we analyzed data covering publication counts, citation analysis, co-authorship, co- occurrence of keywords and thematic development to map the intellectual landscape and collaborative networks in this field.

Results: The analysis included more than 500 articles published between 2021 and 2024. The United States, Germany and the United Kingdom were the top contributors to this field. The study highlights that neural network applications in diagnostic imaging, natural language processing for clinical documentation, and patient data in the field of general internal medicine, radiology, medical informatics, health care services, surgery, oncology, ophthalmology, neurology, orthopedics and psychiatry have seen significant growth in publications over the past two years. Keyword trend analysis revealed emerging sub-themes such as clinical research, artificial intelligence, ChatGPT, education, natural language processing, clinical management, virtual reality, chatbot, indicating a shift towards addressing the broader implications of LLM application in healthcare.

Conclusion: The use of LLM in healthcare is an expanding field with significant academic and clinical interest. This bibliometric analysis not only maps the current state of the research, but also identifies important areas that require further research and development. Continued advances in this field are expected to significantly impact future healthcare applications, with a focus on increasing the accuracy and personalization of patient care through advanced data analytics.

Keywords: artificial intelligence; chatbot; clinical applications; diagnosis; healthcare; large language models; treatment recommendations.

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Conflict of interest statement

The authors state that there are no personal relationships or financial interests that could seem to have influenced the work reported in this paper.

Figures

Figure 1
Figure 1
Flowchart for the study.
Figure 2
Figure 2
Global trend of publications and citations.
Figure 3
Figure 3
Top countries or regions. (A) Geographic distribution of publications. (B) A network visualization map of countries or regions.
Figure 4
Figure 4
Cooperation and citations between Institutions. (A) Most referenced institutions. (B) A network visualization map of institutions.
Figure 5
Figure 5
Authors collaborate. (A) The most productive authors. (B) The most cited authors.
Figure 6
Figure 6
Collaboration among authors. (A) Authors’ visualization map. (B) A visual map of the most cited authors.
Figure 7
Figure 7
Web of Science research areas.
Figure 8
Figure 8
Keyword co-occurrence analysis and network visualization map.
See this image and copyright information in PMC

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