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Development and validation of an artificial intelligence model for basal cell carcinoma detection and section completeness assessment in Mohs micrographic surgery in an Asian cohort.

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Title: Development and validation of an artificial intelligence model for basal cell carcinoma detection and section completeness assessment in Mohs micrographic surgery in an Asian cohort.
Authors: Loo LY; Department of Dermatology, Singapore General Hospital, Singapore., Luo WB; Department of Anatomical Pathology, Singapore General Hospital, Singapore., Hong-Kei Lam I; Department of Anatomical Pathology, Singapore General Hospital, Singapore., Heng SY; Department of Anatomical Pathology, Singapore General Hospital, Singapore., Cheo FF; Department of Anatomical Pathology, Singapore General Hospital, Singapore.; Duke-National University of Singapore Academic Medical Centre, Singapore., Huang J; AI Singapore (ex-AI Apprenticeship Programme), Singapore., Song SW; AI Singapore (ex-AI Apprenticeship Programme), Singapore., Yeoh T; AI Singapore (ex-AI Apprenticeship Programme), Singapore., Feng FX; AI Singapore, Singapore., Baharudin SN; AI Singapore, Singapore., Khor LY; Department of Anatomical Pathology, Singapore General Hospital, Singapore.; Duke-National University of Singapore Academic Medical Centre, Singapore., Cheng CL; Department of Anatomical Pathology, Singapore General Hospital, Singapore.; Duke-National University of Singapore Academic Medical Centre, Singapore., Tang PY; Department of Anatomical Pathology, Singapore General Hospital, Singapore.; Duke-National University of Singapore Academic Medical Centre, Singapore., Oh CC; Department of Dermatology, Singapore General Hospital, Singapore.; Duke-National University of Singapore Academic Medical Centre, Singapore.
Source: JAAD international [JAAD Int] 2026 Sep 04; Vol. 29, pp. 15-24. Date of Electronic Publication: 2026 Sep 04 (Print Publication: 2026).
Publication Type: Journal Article
Journal Info: Publisher: Elsevier Inc Country of Publication: United States NLM ID: 101774762 Publication Model: eCollection Cited Medium: Internet ISSN: 2666-3287 (Electronic) Linking ISSN: 26663287 NLM ISO Abbreviation: JAAD Int Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2666-3287
DOI:10.1016/j.jdin.2026.08.010