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Updated: Jul 13, 2026
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
Mapping the structure of an emerging field: A scientometric decoding of large language model applications in the
Yue Lin1, Yiseul Choi1,2, Wonse Park1,2
1Department of Advanced General Dentistry, Yonsei University College of Dentistry, Seoul 03722, Republic of Korea.
Objectives:
To map the knowledge structure of large language model (LLM) applications in the dental field (LADF) through a dual-database scientometric study that highlights trends, collaborations, hotspots, and future directions.
Materials And Methods:
The Web of Science and Scopus were searched on August 1, 2025. LLM-related articles in dentistry were screened. After removing duplicates, 311 English articles and reviews were included in the study. Bibliometrix (v5.0) was used for characteristic and citation analyses. CiteSpace (v6.4.R1) was used for keyword analysis.
Results:
The number of publications surged from 24 (2023) to 182 (Jan-Aug 2025). Among all publications, 90% were original research articles. Two-thirds were open-access, funded mainly by governments. As for the collaboration network, author and institutional networks were fragmented. National-level collaboration was stronger. High-income countries dominated the LADF output. Keyword clustering revealed a hub-and-spoke structure that included three research frontiers: 1. broadening applications, 2. deepening clinical use, and 3. comparative evaluation, with accuracy and quality as central themes.
Conclusions:
LADF has expanded rapidly, but research remains fragmented. Shared datasets, stronger global collaboration, and the development of standardized evaluation metrics, particularly for diagnostic and question-answering proficiencies, are necessary to address the central themes of accuracy and quality.