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Related Experiment Video

Updated: Jun 9, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Comparing the dental knowledge of large language models.

Camila Tussie¹, Abraham Starosta²

¹Harvard School of Dental Medicine, Boston, Massachusetts, USA. camitussie@hsdm.harvard.edu.

British Dental Journal

|November 1, 2024

Summary

ChatGPT-4 demonstrated superior dental knowledge among tested large language models (LLMs), achieving 75.88% accuracy on Integrated National Board Dental Examination questions. This highlights the importance of LLM selection for dental applications.

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Area of Science:

Artificial Intelligence in Dentistry
Natural Language Processing Applications

Background:

Large language models (LLMs) offer potential for AI integration in dentistry, impacting clinical practice, administration, and education.
Accurate dental knowledge in LLMs is crucial to mitigate risks in patient care and education.

Purpose of the Study:

To compare the dental knowledge accuracy of different LLMs.
To evaluate LLM performance on Integrated National Board Dental Examination (INBDE) questions.

Main Methods:

Assessed closed-source and open-source LLMs.
Analyzed responses to INBDE patient-box and multiple-choice questions.

Main Results:

ChatGPT-4 achieved the highest accuracy (75.88%), followed by Claude-2.1 (66.38%) and Mistral-Medium (54.77%).
Statistically significant performance differences were observed across all evaluated LLMs.

Conclusions:

LLMs show significant potential for dental field integration.
Careful selection of LLMs is vital for developing reliable dental technologies.
Limitations require addressing before unsupervised clinical deployment of LLMs.