Translation of frozen sections into FFPE images for skin cancer resection margins using generative AI

Frozen section (Frozen) analysis is essential for intraoperative margin assessment in skin cancer surgery, but assessment is limited by freezing artifacts that obscure cellular detail. Formalin-fixed paraffin-embedded (FFPE) histopathology, the gold standard, provides higher quality but requires >24 h for processing. Herein, we developed and validated generative AI models to translate Frozen images into AI-generated FFPE images (GenFFPE) using 2594 slides from 283 cases across five major skin cancer types. Four unpaired image-to-image models (CycleGAN, CUT, AIFFPE, and SANTA) were trained and compared using quantitative metrics and expert rankings; CUT demonstrated the best overall fidelity. External validation and a visual Turing test (accuracy 60.2%) confirmed image realism. Among 55 discrepant cases, GenFFPE-based reassessment increased diagnostic concordance by 53.3%. AI-based Frozen-to-FFPE translation is, thus, feasible and clinically meaningful, offering a potential tool to improve intraoperative diagnostic reliability and support decision-making for challenging tumor types such as extramammary Paget's disease.