Benchmarking large-language-model vision capabilities in oral and maxillofacial anatomy: A cross-sectional study

Currently, publicly available multimodal LLMs can only moderately identify oral and maxillofacial landmarks, and no endpoint is sufficiently reliable to serve as a stand-alone answer key. Higher accuracy was achievable with a trade-off in latency, highlighting the need for domain-specific tuning and human oversight. This atlas benchmark study introduced here provides a reproducible yardstick for future model refinement and educational integration.