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April 29, 2026

Temporal Trends in Large Language Model (LLM) Accuracy: A Meta-Analysis of Multiple-Choice Question Performance in Dentistry and Dental Education.

Alison F. Doubleday Colleen M. Cheverko Olena Bolgova Volodymyr Mavrych Fathima Raahima Riyas Mohamed Jennifer Westrick Lorena Juarez Emily Rush Kathryn Solka Jessica Byram Robert Beacker Victoria Gomez Brenda Anak Ganeng Leslie Hoffman Victoria Roach Kirsten Brown Nicole DeVaul Colleen Garnett Hannah Herriott Rebecca Lufler Jason Mussell Joy Balta Michael Pascoe Jason W. Middleton Sharon Duffy Georgina Stephens Adam B. Wilson

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Key Points

This research aims to analyze the accuracy trends of Large Language Models (LLMs) on multiple-choice questions (MCQs) in dentistry over time.
Performed a meta-analysis of studies published from January 2022 to August 2025.
Followed PICO model for study eligibility and utilized proportional meta-analysis for pooled accuracy measurement.
Conducted meta-regression to examine heterogeneity sources.
Twenty-seven articles were included in the analysis.
LLM accuracy in answering dentistry MCQs has significantly improved over time (exact p-value not reported).

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Implication

Meta-analysis evaluates improving accuracy of large language models on dentistry MCQs, suggesting educational integration potential.

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Cite This Study

Doubleday et al. (2026) studied this question.

synapsesocial.com/papers/69f837003ed186a7399811ba https://doi.org/10.1016/j.jdent.2026.106724

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