Background: Artificial intelligence (AI) has recently surfaced as a research topic in dermatology and dermatopathology. In a recent survey, dermatologists were overall positive toward a development with an increased use of AI, but little is known about the corresponding attitudes among pathologists working with dermatopathology. The objective of this investigation was to make an inventory of these attitudes.Participants and Methods: An anonymous and voluntary online survey was prepared and distributed to pathologists who regularly analyzed dermatopathology slides/images. The survey consisted of 39 question divided in five sections; (1) AI as a topic in pathology; (2) previous exposure to AI as a topic in general; (3) applications for AI in dermatopathology; (4) feelings and attitudes toward AI and (5) self-reported tech-savviness and demographics. The survey opened on March 13, 2020 and closed on May 5, 2020.Results: Overall, 718 responders (64.1% females) representing 91 countries were analyzed. While 81.5% of responders were aware of AI as an emerging topic in pathology, only 18.8% had either good or excellent knowledge about AI. In terms of diagnosis classification, 42.6% saw strong or very strong potential for automated suggestion of skin tumor diagnoses. The corresponding figure for inflammatory skin diseases was 23.0% (P adj <0.0001). For specific applications, the highest potential was considered for automated detection of mitosis (79.2%), automated suggestion of tumor margins (62.1%) and immunostaining evaluation (62.7%). The potential for automated suggestion of immunostaining (37.6%) and genetic panels (48.3%) were lower. Age did not impact the overall attitudes toward AI. Only 6.0% of the responders agreed or strongly agreed that the human pathologist will be replaced by AI in the foreseeable future. For the entire group, 72.3% agreed or strongly agreed that AI will improve dermatopathology and 84.1% thought that AI should be a part of medical training.Polesie et al. Attitudes Toward AI Within DematopathologyConclusions: Pathologists are generally optimistic about the impact and potential benefit of AI in dermatopathology. The highest potential is expected for narrow specified tasks rather than a global automated suggestion of diagnoses. There is a strong need for education about AI and its use within dermatopathology.

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Lentigo maligna (LM) represents an overgrowth of atypical melanocytes at the dermal-epidermal junction of chronically sun-damaged skin. The presence of LM on sun-damaged skin poses a diagnostic challenge because the solar-induced melanocytic hyperplasia makes it difficult to assess the LM margins. Melanocytic density can be used to discriminate sun-damaged skin from LM. The aim of this study was to quantify the melanocytic density at the surgical margins of scanned whole-slide images of LM comparing sections stained with H&E and SOX10. Twenty-six surgically excised LM diagnosed at the Department of Pathology at Sahlgrenska University Hospital were collected. The slides that contained the closest surgical margin or harbored the highest density of melanocytes at the margin were selected for serial sectioning using H&E and SOX10. Whole-slide imaging at •40 magnification was used, and a circular field with a diameter of 0.5 mm at the surgical margin was superimposed on the image. Five blinded pathologists reviewed the slides in a randomized order. In the majority of the cases (24/26), the pathologists identified more melanocytes on the SOX10 slides than those on the H&E slides. On average, 2.5 times more melanocytes were counted using SOX10 compared with H&E (P , 0.05). Furthermore, the average group SD on the H&E slides was 4.12 compared with 2.83 on the SOX10 slides (P = 0.004). Thus, the use of SOX10 staining leads to higher melanocytic density counts compared with H&E staining when assessing the surgical margins of LM. The use of SOX10 staining also significantly decreased the interobserver variability between pathologists.