Comparison of adhesion scoring performance between humans and large language models in experimental rat laparotomy

This study compared the macroscopic adhesion scoring performance of large language models (LLMs: ChatGPT-o3, ChatGPT-5, Gemini-2.5 Pro) with that of novice veterinary surgeons, using expert consensus as the reference. Eighty standardized postoperative laparotomy cases in Wistar rats were photographed and scored using the Nair 0-4 adhesion scale. Two novice surgeons and three LLMs independently evaluated each case; the expert reference was defined by a surgeon and a pathologist. Group differences were analyzed using the Kruskal-Wallis test with Dunn-Bonferroni post hoc comparisons, correlations by Bonferroni-adjusted Spearman coefficients, human interobserver reliability by intraclass correlation coefficient (ICC) (A,1), and agreement with the expert by quadratic-weighted Cohen's κ and exact-match accuracy. Overall differences were significant. ChatGPT-o3, ChatGPT-5, Gemini-2.5 Pro, and Novice 1 assigned lower scores, while Novice 2 assigned higher scores. Correlations with the expert were significant for Novice 1 (ρ = 0.706), Novice 2 (ρ = 0.593), and ChatGPT-o3 (ρ = 0.617), but not for ChatGPT-5 or Gemini-2.5 Pro. Inter-observer reliability among human raters was moderate (ICC = 0.55). Importantly, absolute exact-match accuracies were modest across all evaluators, with the highest accuracy observed for Novice 1 (33.8%) and ⩽26.3% for the LLMs. While novices outperformed the models, these findings highlight the intrinsic difficulty of fine-grained Nair 0-4 adhesion scoring on two-dimensional intraoperative images and indicate that current LLMs are better suited as calibrated decision-support tools rather than stand-alone raters.