From STM to GPT: A Comparative Study of Topic Modeling Methods for AI in Dentistry
This study presents a comprehensive topic modeling analysis of scientific abstracts in the field of artificial intelligence (AI) applied to dentistry. Three complementary approaches were compared: Structural Topic Modeling (STM); embedding-based clustering using the Leiden algorithm on OpenAI text embeddings; and zero-shot GPT-based topic modeling
Authors
- П. В. Середин (ORCID: https://orcid.org/0000-0002-6724-0063)
- Tatiana Litvinova (ORCID: https://orcid.org/0000-0002-6019-3700)
Publication Details
- Journal
- RESEARCH RESULT Theoretical and Applied Linguistics
- Published
- 2025-09-30
- DOI
- https://doi.org/10.18413/2313-8912-2025-11-3-0-5
- Primary Topic
- Computational and Text Analysis Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00