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Evaluating Retrieval-Augmented Large Language Models on External Cervical Resorption: A Comparative Study of Gemini

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Summary

Two AI models, Google Gemini and NotebookLM, showed high accuracy and consistency in answering clinical questions about external cervical resorption. NotebookLM performed slightly better, but retrieval augmentation did not significantly improve responses for these tasks.

Keywords:
Artificial intelligenceendodonticsexternal cervical resorptionlarge language modelsretrieval-augmented generation

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Area of Science:

  • Artificial Intelligence in Dentistry
  • Clinical Decision Support Systems
  • Natural Language Processing in Healthcare

Background:

  • This study assessed the accuracy and consistency of two Alphabet Inc. large language models: Google Gemini (GG) and NotebookLM (NLM).
  • The evaluation focused on answering clinical questions related to external cervical resorption using a retrieval-augmented framework.
  • NotebookLM is a document-grounded configuration, while Google Gemini was used in its base configuration.

Purpose of the Study:

  • To evaluate the accuracy and consistency of Google Gemini and NotebookLM in responding to clinical questions about external cervical resorption.
  • To compare the performance of a base large language model against a document-grounded configuration.
  • To determine if retrieval augmentation significantly impacts the quality of responses for structured clinical tasks.

Main Methods:

  • Forty-six dichotomous clinical questions on external cervical resorption were created by three endodontic experts.
  • Each question was posed to Google Gemini and NotebookLM via three independent user accounts, generating 276 total responses.
  • Responses were independently assessed by three endodontic experts against gold standard answers for accuracy and consistency.

Main Results:

  • Google Gemini achieved 89% accuracy and 93% consistency.
  • NotebookLM achieved 96% accuracy and 90% consistency.
  • No statistically significant differences were found between the two models regarding accuracy and consistency.

Conclusions:

  • Both NotebookLM and Google Gemini demonstrated high accuracy and consistency in answering clinical questions.
  • NotebookLM exhibited a slightly superior performance compared to Google Gemini.
  • Retrieval augmentation did not yield significant improvements for these specific structured clinical questions.