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New generative artificial intelligence model: ScholarGPT's performance on dental avulsion.

Taibe Tokgöz Kaplan¹

¹Karabuk University, Faculty of Dentistry, Department of Pediatric Dentistry, Karabük, Turkey.

International Journal of Medical Informatics

|August 15, 2025

Summary

ScholarGPT, a new large language model (LLM), demonstrated superior performance in answering dental avulsion questions compared to ChatGPT and Gemini. This AI tool shows promise for reliable academic information retrieval.

Keywords:

Artificial intelligence ChatGPT Dental avulsion Gemini Large Language Models ScholarGPT

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Area of Science:

Artificial Intelligence in Dentistry
Large Language Models (LLMs) for Academic Research

Background:

Dental avulsion management requires accurate information.
Evaluating AI performance in specialized medical fields is crucial.

Purpose of the Study:

To assess ScholarGPT's performance on dental avulsion technical questions.
To compare ScholarGPT's efficacy against ChatGPT and Gemini.

Main Methods:

22 technical questions (MCQ, T/F) were administered to ScholarGPT.
Responses were evaluated using a modified Global Quality Scale (GQS).
Scoring involved a single researcher assessing responses across three separate, randomized sessions.

Main Results:

ScholarGPT achieved high scores for both MCQ (4.64) and T/F (4.82) questions.
ScholarGPT's performance was significantly superior to both ChatGPT and Gemini (p < 0.001).
ScholarGPT provided more consistent and higher-quality responses than the other LLMs.

Conclusions:

ScholarGPT exhibits high performance for dental avulsion queries, outperforming existing LLMs.
AI models trained on academic data offer enhanced accuracy and reliability.
Future development of dentistry-specific LLMs could further improve AI-driven information delivery.