Quantifying the speed-accuracy trade-off of large language models on oral and maxillofacial surgery multiple-choice questions.

ABSTRACT: Large language models (LLMs) such as GPT-4o, Copilot and Gemini are entering dental curricula, yet their suitability for real-time decision support remains unclear because most evaluations report accuracy alone. This prospective in silico diagnostic-accuracy study benchmarked six engines-GPT-4o, OpenAI o3, Copilot-Quick, Copilot-Deep, Gemini-Flash and Gemini-Pro-against 1766 single-best-answer multiple-choice questions from a contemporary oral and maxillofacial surgery (OMFS) board-review text. Textbook keys served as the reference standard. Overall and domain-level accuracy, intra-model answer consistency and per-batch response latency were recorded; χ² tests compared accuracies and Kruskal-Wallis with multiplicity-adjusted Mann-Whitney U tests compared response times. Accuracy differed s

SUBMITTER: Nguyen VA 

PROVIDER: S-EPMC12630698 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature