Multiclass diagnostic performance and error patterns of a multimodal large language model in oral histopathology: A...

To evaluate the diagnostic performance, feature-level agreement, and error patterns of a multimodal large language model (MLLM) across the oral dysplasia-malignancy continuum.

In this retrospective diagnostic accuracy study, 300 histopathology images representing normal oral mucosa (n = 100), oral potentially malignant disorders (OPMD) (n = 100), and oral squamous cell carcinoma (OSCC) (n = 100) were evaluated using a standardized two-stage prompting framework consisting of morphological feature extraction followed by structured diagnostic classification. Reference diagnoses were established by three experienced oral pathologists. Interobserver agreement among pathologists was assessed using Fleiss' κ statistics. Primary outcomes included overall accuracy, macro-F1 score, and balanced accuracy. Secondary outcomes included feature-level agreement, confidence-accuracy alignment, and clinically significant error patterns.

Interobserver agreement among the reference pathologists was substantial for the primary three-class diagnosis (Fleiss' κ = 0.81). Overall accuracy was 0.667 (95% confidence interval [CI]: 0.620-0.727), with a macro-F1 score of 0.662 and balanced accuracy of 0.755. Performance varied substantially across diagnostic categories, with high sensitivity for normal mucosa (0.990; 95% CI: 0.968-1.000). Reduced sensitivities were observed for OPMD (0.430; 95% CI: 0.340-0.531) and OSCC (0.580; 95% CI: 0.495-0.688), indicating reduced discriminative ability relative to normal mucosa. Feature-level agreement was generally low, with the weakest agreement for architectural dysplasia features (κ = -0.116 to -0.068), slight agreement for invasion-related features (κ = 0.111-0.120), and the highest agreement was for nuclear hyperchromasia (κ = 0.281). Most misclassifications (84.8%) occurred at the dysplasia-malignancy interface. Proxy calibration analysis demonstrated systematic overconfidence in the histopathological predictions.

The MLLM demonstrated moderate performance in oral histopathology, with reliable recognition of normal tissue and reduced discriminative ability for OPMD and OSCC relative to normal mucosa, particularly at the dysplasia-malignancy interface. These findings support a potential assistive role under expert supervision while highlighting important limitations in clinically critical diagnostic settings.