Background Mohs micrographic surgery for basal cell carcinoma (BCC) requires rapid intraoperative histologic interpretation. This is labour-intensive and incomplete tissue sections compromise margin assessment. Artificial Intelligence (AI) has shown promise in BCC detection but section completeness assessment remains underexplored. Objective Develop and validate an AI model for BCC detection and section completeness assessment on Mohs frozen-section histology. Methods We conducted a retrospective single-centre cohort study using histology slides from 130 patients who underwent Mohs surgery for BCC from 2017-2025. Whole slides were digitised and annotated. A convolutional neural network model was trained to detect BCC and classify section completeness. Model performance assessment with standard performance metrics and region-of-interest evaluation was performed. Results The BCC detection model achieved accuracy 0.993, sensitivity 0.915, specificity 0.996, precision 0.889, F1-score 0.902 at the tile level, with comparable ROI sensitivity (0.910). The section completeness model achieved accuracy 0.890, sensitivity 0.948, specificity 0.524, precision 0.927 and F1-score 0.937. Limitations Single-centre retrospective design, limited representation of rare BCC subtypes, no direct comparison with human experts. Conclusion Although the specificity for section completeness was low, further research and additional model training may enable development of a model with improved performance
