Comparative performance of large language models in answering periodontology questions from the Turkish Dental Specialty Examination: a cross-sectional study on accuracy and coverage.

ABSTRACT:

Background

In recent years, several studies have explored the use of large language models (LLMs) such as ChatGPT-4, Claude, Gemini Advanced, and DeepSeek-R1 in dental education. Nevertheless, no study has yet reported a comparative evaluation of multiple LLMs specifically on the periodontology section of the Turkish Dental Specialty Examination (DUS), nor analyzed how their performance differs between fundamental knowledge questions and clinical decision-making questions. This study aims to fill this gap by comparing the accuracy and coverage performance of four contemporary LLMs on publicly available DUS periodontology questions.

Methods

A total of 60 publicly available periodontology questions from the DUS (2010-2021) were included. Questions were categorized into Basic S

SUBMITTER: Kandemir M 

PROVIDER: S-EPMC12629045 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature