Comparative evaluation of multimodal large language models for Mayo Endoscopic Subscore grading in ulcerative colitis

Table 2 Overall performance comparison of five multimodal large language models in phase A

	Gemini-2.5-Pro	Grok-4	GPT-4o	GPT-5	Qwen-VL-Max
Accuracy	0.502	0.417	0.594	0.717	0.353
Precision	0.639	0.552	0.635	0.731	0.488
Recall	0.502	0.417	0.594	0.717	0.353
F1 score (95%CI)	0.480 (0.452-0.574)	0.415 (0.369-0.481)	0.602 (0.522-0.648)	0.720 (0.665-0.773)	0.338 (0.263-0.382)
Cohen’s κ	0.343	0.239	0.449	0.608	0.133
MAE	0.611	0.770	0.452	0.297	0.767
MSE	0.866	1.194	0.544	0.325	1.021
RMSE	0.930	1.093	0.738	0.570	1.011
SD	0.781	0.918	0.697	0.564	0.953
CV	0.713	0.838	0.637	0.515	0.870
r value1	0.681	0.590	0.777	0.852	0.478

¹Pearson correlation coefficient. MAE: Mean absolute error; MSE: Mean squared error; RMSE: Root mean squared error; CV: Coefficient of variation; SD: Standard deviation; CI: Confidence interval.