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

Table 3 Overall performance comparison of five multimodal large language models in phase B

	Gemini-2.5-Pro	Grok-4	GPT-4o	GPT-5	Qwen-VL-Max
Accuracy	0.541	0.420	0.583	0.710	0.406
Precision	0.621	0.480	0.590	0.724	0.515
Recall	0.541	0.420	0.583	0.710	0.406
F1 score (95%CI)	0.546 (0.490-0.611)	0.426 (0.363-0.481)	0.584 (0.502-0.618)	0.715 (0.664-0.768)	0.408 (0.313-0.439)
Cohen’s κ	0.381	0.229	0.425	0.596	0.190
MAE	0.565	0.753	0.473	0.300	0.707
MSE	0.799	1.141	0.587	0.322	0.940
RMSE	0.894	1.068	0.766	0.567	0.969
SD	0.843	0.973	0.755	0.566	0.951
CV	0.769	0.888	0.689	0.517	0.868
r value1	0.644	0.573	0.749	0.850	0.499

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.