Comparative study of advanced reasoning versus baseline large-language models for histopathological diagnosis in oral and maxillofacial pathology.

ABSTRACT: Large language models (LLMs) are increasingly explored as diagnostic copilots in digital pathology, but whether the newest reasoning-augmented architectures provide measurable benefits over earlier versions is unknown. We compared OpenAI's o3 model, which uses an iterative planning loop, with the baseline GPT-4o on 459 oral and maxillofacial (OMF) cases drawn from standard textbooks. Each case consisted of two to five high-resolution haematoxylin-and-eosin micrographs, and both models were queried in zero-shot mode with an identical prompt requesting a single diagnosis and supporting microscopic features. Overall, o3 correctly classified 31.6% of cases, significantly surpassing GPT-4o at 18.7% (Δ = 12.9%, P < 0.001). The largest gain was recorded for the heterogeneous "other conditions" ca

SUBMITTER: Nguyen VA 

PROVIDER: S-EPMC12755752 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature