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Assessment of the Mouth

Assessment of the Mouth

A thorough mouth assessment, including inspection and palpation of the lips, gums, tongue, tonsils, uvula, and pharynx, is crucial in detecting potential health issues. Diseases ranging from oral cancer to systemic conditions like diabetes could be identified early through careful oral examination. This article provides a detailed guide on conducting a comprehensive mouth assessment.
Mouth Inspection
The inspection begins with visually examining the mouth for symmetry, color, and size.

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Performance of Multimodal Generative AI Models in Addressing Complex Dental Inquiries With Text, Images, and

Hang-Nga Mai^1,2, Du-Hyeong Lee^1,2, Jekita Kaenploy³

¹Institute for Translational Research in Dentistry, Kyungpook National University, Daegu, South Korea.

Journal of Esthetic and Restorative Dentistry : Official Publication of the American Academy of Esthetic Dentistry ... [Et Al.]

|November 25, 2025

Summary

Multimodal large language models (LLMs) show promise for dental education, but performance varies. Claude 3 Sonnet led in accuracy on dental exams, though all models faced challenges with complex clinical data.

Keywords:

dental inquiry exam generative artificial intelligence large language model performance

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Area of Science:

Artificial Intelligence in Dentistry
Natural Language Processing
Machine Learning for Healthcare

Background:

Multimodal large language models (LLMs) integrate text, images, and data, offering potential for dental education and decision support.
Addressing complex, multimodal dental inquiries is a key challenge for current AI systems.

Purpose of the Study:

To evaluate the performance of leading multimodal LLMs in answering dental board examination questions.
To identify factors affecting LLM performance on multimodal dental queries.

Main Methods:

Four multimodal LLMs (ChatGPT-4V, Claude 3 Sonnet, Microsoft 365 Copilot 2024, Google Gemini 1.5 Pro) were tested.
Performance was assessed using Integrated National Board Dental Examination (INBDE) and Advanced Dental Admission Test (ADAT) data.
Statistical analyses included descriptive statistics, chi-squared tests, and Cohen's kappa to compare model agreement and accuracy.

Main Results:

Claude 3 Sonnet demonstrated the highest accuracy on both INBDE and ADAT exams.
Significant performance differences were observed between models on the ADAT, but not the INBDE.
Common errors involved misinterpreting clinical scenarios, visual data, and dental terminology.

Conclusions:

Multimodal LLMs have potential for dental applications, but model performance varies significantly.
Challenges remain in accurately interpreting complex clinical data, visual information, and ambiguous terminology.
Effective utilization requires understanding model differences and managing complex clinical data.