Trends, Challenges, and Applications of Large Language Models in Healthcare: A Bibliometric and Scoping Review

Vincenza Carchiolo () and Michele Malgeri ()
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Vincenza Carchiolo: Dipartimento Ingegneria Elettrica Elettronica e Informatica, Università di Catania, Via Santa Sofia 64, 95125 Catania, Italy
Michele Malgeri: Dipartimento Ingegneria Elettrica Elettronica e Informatica, Università di Catania, Via Santa Sofia 64, 95125 Catania, Italy

Future Internet, 2025, vol. 17, issue 2, 1-32

Abstract: The application of Large Language Models ( LLM s) in medicine represents an area of growing interest in scientific research. This study presents a quantitative review of the scientific literature aiming at analyzing emerging trends in the use of LLM s in the medical field. Through a systematic analysis of works extracted from Scopus, the study examines the temporal evolution, geographical distribution, and scientific collaborations between research institutions and nations. Furthermore, the main topics addressed in the most cited papers are identified, and the most recent and relevant reviews are explored in depth. The quantitative approach enables mapping the development of research, highlighting both opportunities and open challenges. This study presents a comprehensive analysis of research articles and review-type articles across several years, focusing on temporal, geographical, and thematic trends. The temporal analysis reveals significant shifts in research activity, including periods of increased or decreased publication output and the emergence of new areas of interest. Geographically, the results identify regions and countries with higher concentrations of publications, as well as regions experiencing growing or stagnant international collaboration. The thematic analysis highlights the key research areas addressed in the reviewed papers, tracking evolving topics and changes in research focus over time. Additionally, the collaborative analysis sheds light on key networks of international collaboration, revealing changes in the distribution of affiliations across subperiods and publication types. Finally, an investigation of the most cited papers highlights the works that have had the greatest impact on the scientific community, identifying enduring themes and methodologies that continue to shape the field of study. The results provide a clear overview of current trends and future perspectives for the application of LLM s in medicine, offering a valuable reference for researchers and professionals in the field.

Keywords: large language models; artificial intelligence; healthcare; e-health; deep learning; transformer; neural networks; review; bibliometric analysis (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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