Generative Pre-trained Transformer: Trends, Applications, Strengths and Challenges in Dentistry: A Systematic Review
10.4258/hir.2025.31.2.189
- Author:
Sibyl SILUVAI
1
;
Vivek NARAYANAN
;
Vinoo Subramaniam RAMACHANDRAN
;
Victor Rakesh LAZAR
Author Information
1. Department of Public Health Dentistry, SRM Kattankulathur Dental College and Hospital, SRM Institute of Science and Technology, Tamilnadu, India
- Publication Type:Original Article
- From:Healthcare Informatics Research
2025;31(2):189-199
- CountryRepublic of Korea
- Language:English
-
Abstract:
Objectives:The integration of large language models (LLMs), particularly those based on the generative pre-trained transformer (GPT) architecture, has begun to revolutionize various fields, including dentistry. Despite these promising applications, the use of GPT in dentistry presents several challenges. Ongoing research and the development of robust ethical frameworks are essential to mitigate these issues and enhance the responsible deployment of GPT technologies in clinical settings. Hence, this systematic review aims to explore the trends, applications, strengths, and challenges associated with the use of GPT in dentistry.
Methods:Articles were selected if they contained detailed information on the application of GPT in dentistry. The search strategy used in systematic reviews follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our search of databases and other sources yielded a total of 704 studies. After removing duplicates and conducting a full-text screening, 16 articles were included in the review. The methodological quality of the research was evaluated using the Critical Appraisal Skills Programme (CASP) checklist.
Results:Out of a total of 91 articles published on GPT in dentistry, 20 were editorials and 11 were narrative reviews; these were excluded, leaving 60 original research articles for further analysis. The articles were assessed based on the type of results they provided. Ultimately, 16 articles that reported positive findings with robust methodology were included in this review
Conclusions:The results highlight mixed responses; therefore, further research on integration into clinical workflows must be conducted with extensive methodological rigor.
