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July 6, 2026BMC Oral HealthOpen Access

A comparative evaluation of large language models in diagnosis and treatment planning in restorative dentistry

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Authors

ETEbru İrem TekeGaziantep UniversityABAyşenur Güngör BorsökenGaziantep University

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Implication

Comparative evaluation of language models for diagnosis and treatment planning in restorative dentistry, indicating their potential utility and limitations.

Key Points

  • This research evaluates the accuracy of five different large language models (LLMs) in diagnosing and planning treatments in restorative dentistry.
  • Formulated 20 common restorative dentistry cases into questions for LLM evaluation.
  • Evaluated responses from five LLMs by 42 specialists using a Likert scale.
  • Analyzed data with non-parametric Kruskal-Wallis test and Dunn multiple comparison test.
  • Significant differences observed among models for 15 out of 20 questions (p < 0.05).
  • Google Gemini 3 Flash and ChatGPT-5 received the highest median scores (83 and 81, respectively).
  • Claude Sonnet 4.5 and Google Gemini 3 Flash provided the highest reference accuracy (85.7% and 84.2% respectively), while DeepSeek V3.2 had a fabrication rate of 55.6%.

Cite This Study

Teke et al. (2026) studied this question.

synapsesocial.com/papers/6a4b44fa997070ff83b5b016https://doi.org/10.1186/s12903-026-09154-0
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