Artificial Intelligence for Mohs and Dermatologic Surgery: A Systematic Review and Meta-Analysis.

Over the past decade, several studies have shown that potential of artificial intelligence (AI) in dermatology. However, there has yet to be a systematic review evaluating the usage of AI specifically within the field of Mohs micrographic surgery (MMS). In this review, we aimed to comprehensively evaluate the current state, efficacy, and future implications of AI when applied to MMS for the treatment of nonmelanoma skin cancers (NMSC). A systematic review and meta-analysis was conducted following PRISMA guidelines across several databases, including PubMed/MEDLINE, Embase, and Cochrane libraries. A predefined protocol was registered in PROSPERO, with literature search involving specific keywords related to AI and Mohs surgery for NMSC. From 23 studies evaluated, our results find that AI shows promise as a prediction tool for precisely identifying NMSC in tissue sections during MMS. Furthermore, high AUC and concordance values were also found across the various usages of AI in MMS, including margin control, surgical recommendations, similarity metrics, and in the prediction of stage and construction complexity. The findings of this review suggest promising potential for AI to enhance the accuracy and efficiency of Mohs surgery, particularly for NMSC.

Mirza FN ，Haq Z ，Abdi P ，Diaz MJ ，Libby TJ ... -  《-》

Mohs Micrographic Surgery: Past, Present, and Future.

Cohen DK ，Goldberg DJ 《-》

Surgical Margins of Nonmelanoma Skin Cancers in Mohs Surgery: Dermoscopy Versus Naked Eye.

Mohs micrographic surgery (MMS) offers high cure rates for nonmelanoma skin cancer (NMSC) but relies on precise margin assessment, traditionally initiated by the naked eye. Dermoscopy has emerged as a potential tool to improve margin delineation. To compare the efficacy of dermoscopic versus naked eye evaluation in marking initial MMS margins for NMSC. A multicenter, prospective study on patients with NMSC undergoing MMS was conducted. Patients were randomized into dermoscopy and naked eye groups. Statistical analysis was performed using statistical package for social sciences. A total of 127 patients were enrolled, with 63 in the naked eye group and 64 in the dermoscopy group. No significant differences in age, sex distribution, or histological subtype were found. The average number of MMS stages and accuracy of initial margins were comparable between groups. Despite its potential, the authors' study found no significant reduction in total MMS stages using dermoscopy. Novel imaging modalities or standardized dermoscopic criteria should be explored to enhance margin assessment accuracy and improve outcomes in NMSC treatment. Dermoscopic evaluation of presurgical margins does not effectively assess lateral/deep margins nor contribute to a reduction in total MMS stages. Improving the authors' understanding of presurgical margin assessment techniques can lead to better treatment outcomes for NMSC.

Aoki KC ，Lazzara D ，Bartos G ，Weiss E ，Saleeby ER ... -  《-》

Mohs appropriate use criteria: retrospectively applied to nonmelanoma skin cancers at a single academic center.

Chong T ，Tristani-Firouzi P ，Bowen GM ，Hadley ML ，Duffy KL ... -  《-》

Artificial intelligence for nonmelanoma skin cancer.

Nonmelanoma skin cancers (NMSCs) are among the top five most common cancers globally. NMSC is an area with great potential for novel application of diagnostic tools including artificial intelligence (AI). In this scoping review, we aimed to describe the applications of AI in the diagnosis and treatment of NMSC. Twenty-nine publications described AI applications to dermatopathology including lesion classification and margin assessment. Twenty-five publications discussed AI use in clinical image analysis, showing that algorithms are not superior to dermatologists and may rely on unbalanced, nonrepresentative, and nontransparent training data sets. Sixteen publications described the use of AI in cutaneous surgery for NMSC including use in margin assessment during excisions and Mohs surgery, as well as predicting procedural complexity. Eleven publications discussed spectroscopy, confocal microscopy, thermography, and the AI algorithms that analyze and interpret their data. Ten publications pertained to AI applications for the discovery and use of NMSC biomarkers. Eight publications discussed the use of smartphones and AI, specifically how they enable clinicians and patients to have increased access to instant dermatologic assessments but with varying accuracies. Five publications discussed large language models and NMSC, including how they may facilitate or hinder patient education and medical decision-making. Three publications pertaining to the skin of color and AI for NMSC discussed concerns regarding limited diverse data sets for the training of convolutional neural networks. AI demonstrates tremendous potential to improve diagnosis, patient and clinician education, and management of NMSC. Despite excitement regarding AI, data sets are often not transparently reported, may include low-quality images, and may not include diverse skin types, limiting generalizability. AI may serve as a tool to increase access to dermatology services for patients in rural areas and save health care dollars. These benefits can only be achieved, however, with consideration of potential ethical costs.

Trager MH ，Gordon ER ，Breneman A ，Weng C ，Samie FH ... -  《-》