AI + real-time margin assessment is entering Mohs surgery: from conventional frozen-section interpretation toward digital/AI-assisted and label-free intraoperative margin assessment.

Machine-learning–guided bioelectrical impedance mapping has been evaluated as an adjunct during Mohs surgery, generating probability maps for BCC at the surgical margin while retaining conventional histopathology as the reference standard. 

Another particularly interesting development is two-photon fluorescence microscopy, which can produce images very similar  H&E preparetions from fresh Mohs specimens,  without  the need of a conventional slide preparation. A 2026 JAAD study evaluated 133 BCC specimens and addresses one of the fundamental limitations of Mohs surgery: the time and competences  required for frozen-section processing. 

This  process testing is opening  a possible change of the Mohs workflow  to become:

Surgery → rapid digital imaging → AI-assisted margin interpretation → targeted additional excision

rather than : 

Surgery → frozen section → manual microscopic interpretation.

These  technologies are currently a possible   implementation  rather than a replacement for the Mohs surgeon and histopathology.

Ceres DM, Gharia M, Harvey K, et al. Machine learning-guided bioelectrical impedance mapping for rapid adjunctive margin assessment in Mohs surgery. npj Biomedical Innovations. 2026;3:47. doi:10.1038/s44385-026-00095-5.

Diagnostic accuracy of two-photon fluorescence microscopy in Mohs surgery of basal cell carcinomas. J Am Acad Dermatol. 2026;95:734-740. doi:10.1016/j.jaad.2026.05.011.

Kantor J. Highlights from JAAD International November 2026: Artificial intelligence and Mohs micrographic surgery. J Am Acad Dermatol. 2026. doi:10.1016/j.jaad.2026.08.094.

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