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

Figure 2 Comprehensive performance analysis of five multimodal large language models. A: Overall ranking of five multimodal large language models based on composite performance scores; B: Comparison of core evaluation metrics (accuracy, F1 score, κ, precision, recall). Statistical tests: McNemar for accuracy, precision, recall, and F1 score; paired Wilcoxon for κ (P < 0.05 considered significant); C: Radar chart illustrating performance across five key dimensions; D: Heatmap summarizing six quantitative indicators (accuracy, precision, recall, F1 score, κ, and mean absolute error); E: Precision-recall scatter plot demonstrating the trade-off between precision and recall; F: Comparison of error metrics (mean absolute error and root mean squared error) where lower values indicate superior performance. MAE: Mean absolute error; RMSE: Root mean squared error.