Weakly supervised deep learning for cutaneous squamous and basal cell carcinoma in whole-slide histopathology.

Model discrimination was quantified using ROC curves, while accuracy, sensitivity, and specificity were reported alongside 95% Wilson confidence intervals (CIs).

On the internal test set, the model achieved perfect classification [area under the receiver operating characteristic (AUC) = 1.0; 100% accuracy, sensitivity, and specificity].

In zero-shot evaluation on the internal test set, HistoGPT achieved an overall accuracy of 77%, with high class-wise sensitivity for basal cell carcinoma (98%, 95% CI 91-100) but markedly reduced sensitivity for cSCC (33%, 95% CI 19-52).