Accuracy of Large Language Models in Answering Dental Examination Questions: A Systematic Review and Meta-Analysis
Abstract
Introduction: Large language models (LLMs), including OpenAI's GPT family accessed via interfaces such as ChatGPT and Microsoft Copilot, as well as non-GPT systems such as Google Gemini, are increasingly applied in healthcare and dental education. However, the accuracy of these systems in specialized tasks such as answering dental examination questions remains unclear.
Methods: This systematic review and meta-analysis evaluated LLM performance in answering dental questions. Databases searched were PubMed, Embase, Scopus, and Web of Science. Data on question type and number, LLM versions, and accuracy rates were extracted. Pooled accuracy was estimated using a random-effects model; heterogeneity and publication bias were assessed.
Results: A total of 39 studies were included, with ChatGPT-4 being the most frequently evaluated model. The pooled accuracy for LLMs was 63.7% (95% CI: 60.3%-67.1%), with high heterogeneity (I² = 91.5%). Subgroup analysis revealed ChatGPT-4 and Copilot (a GPT-based interface) achieved the highest pooled accuracies (∼73% and ∼75%, respectively). Direct comparisons confirmed ChatGPT-4 significantly outperformed earlier versions and some competitor models. Sensitivity analyses supported the robustness of findings.
Conclusion: LLMs demonstrate moderate accuracy in answering dental examination questions and are currently insufficient for autonomous clinical decision-making. When their limitations are explicitly recognized, however, these systems may…
How strong is the evidence?
Evidence Hub reads this as a meta-analysis, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.