Artificial intelligence (AI) has emerged as a hot topic within dermatology and during the last years several studies have demonstrated its benefits in a research setting. While this development is unravelling rapidly and has also been made available to consumers, little is known about the attitudes towards AI among dermatologists. To increase our understanding of dermatologists’ attitudes towards AI within dermatology we prepared an anonymous and voluntary online survey including 29 questions and distributed it to dermatologists through several online channels including mailing lists to members of the International Dermoscopy Society.

Use of artificial intelligence, or machine learning, to assess dermoscopic images of skin lesions to detect melanoma has in several retrospective studies shown high levels of diagnostic accuracy on par with, or even outperforming, experienced dermatologists. However, the enthusiasm around these algorithms has not yet been matched by prospective clinical trials performed in authentic clinical settings. In several European countries, including Sweden, the initial clinical assessment of suspected skin cancer is principally conducted in the primary health care setting by primary care physicians; with or without access to teledermoscopic support from dermatology clinics. To determine the diagnostic performance of an artificial intelligence-based clinical decision support tool for cutaneous melanoma detection, operated by a smartphone application (app), when used prospectively by primary care physicians to assess skin lesions of concern due to some degree of melanoma suspicion. This prospective, multicentre, clinical trial was conducted at 36 primary care centres in Sweden. The physicians used the smartphone app on skin lesions of concern by photographing them dermoscopically, which resulted in a dichotomous decision support text regarding evidence for melanoma. Regardless of the app outcome, all lesions underwent standard diagnostic procedure, by surgical excision or referral to dermatologist. After completed investigation, lesion diagnoses were collected from the patients’ medical records and compared to app outcome and other lesion data. In total, 253 lesions of concern in 228 patients were included, of which 21 proved to be melanomas, with 11 thin invasive melanomas and 10 melanomas in situ. The app’s accuracy (95% confidence interval) in identifying melanomas was reflected in an area under the receiver operating characteristic (AUROC) curve of 0.960 (0.928-0.980), corresponding to a maximum sensitivity and specificity of 95.2% and 84.5%, respectively. For invasive melanomas alone, the AUROC was 0.988 (0.965–0.997), corresponding to a maximum sensitivity and specificity of 100% and 92.6%, respectively. The clinical decision support tool evaluated in this investigation showed high diagnostic accuracy when used prospectively on primary care patients, which could add significant clinical value for primary care physicians in assessing skin lesions to detect melanoma. ClinicalTrials.gov Identifier: NCT05172232

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. Conclusions: 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.

Background: Methotrexate (MTX) use has been suspected of increasing the risk of skin cancer. The aim of this investigation was to examine the association between the use of MTX and the risk of basal cell carcinoma (BCC), cutaneous squamous cell carcinoma (cSCC) and cutaneous malignant melanoma (CMM). Methods: In a nationwide Danish case–control study, we identified incident, histologically verified cases of BCC (n = 131,447), cSCC (n = 18,661) or CMM (26,068) from 2004 to 2018. We matched 10 controls to each case on sex and birth year using risk-set sampling and computed crude and adjusted odds ratios (ORs) using conditional logistic regression for the use of MTX (≥2.5 g) compared with never-use. Results: Use of MTX was associated with increased risk of BCC, cSCC and CMM with adjusted ORs of (95% confidence interval) 1.29 (1.20–1.38), 1.61 (1.37–1.89) and 1.35 (1.13–1.61), respectively. For BCC and cSCC, ORs increased with higher cumulative doses. When restricting the study population to patients with psoriasis, the ORs were 1.43 (1.23–1.67), 1.18 (0.80–1.74) and 1.15 (0.77–1.72), respectively. Conclusions: We observed an increased risk of BCC and cSCC associated with the use of MTX with evidence of a dose–response pattern; however, the association was not consistent when restricting the study population to patients with psoriasis.

Background: Porokeratosis is a clinically heterogeneous group of keratinization disorders with a genetic background mainly affecting the mevalonate pathway, which is involved in the synthesis of cholesterol, an essential component for the formation of the extracellular lipid lamellae in the stratum corneum. Porokeratosis is reportedly associated with an increased risk of keratinocyte cancer, but to date, no large epidemiological studies have been conducted to further address this association.Objectives: The first objective was to characterize a cohort of patients diagnosed with porokeratosis at the Department of Dermatology and Venereology, Sahlgrenska University Hospital (SU), Gothenburg, Sweden. The second objective was to conduct a nationwide registry‐based cohort study to investigate the association, if any, between porokeratosis and the cutaneous malignancies squamous cell carcinoma (SCC), basal cell carcinoma (BCC) and melanoma.Methods: For the SU cohort, the hospital registry was searched for patients with a diagnosis of porokeratosis recorded between 2016 and 2020. Clinical data were extracted from the records of the identified patients. For the nationwide cohort, national registries were searched to identify patients with a diagnosis of porokeratosis between 2001 and 2020. A tenfold control cohort was formed by Statistics Sweden. The data was cross‐referenced with the Swedish Cancer Register to study the associations between porokeratosis and SCC, BCC and melanoma.Results: Disseminated superficial actinic porokeratosis was the most common clinical type among the 108 patients in the SU cohort. In the nationwide search, 2277 patients with porokeratosis were identified (prevalence 1/4132). Porokeratosis was associated with an increased risk for SCC, BCC and melanoma with hazard ratios (95% CI) of 4.3 (3.4–5.4), 2.42 (1.97–2.98) and 1.83 (1.18–2.82), respectively, in the patient cohort, compared to the matched control group.Conclusion: Porokeratosis is a common genodermatosis, and it is associated with an enhanced risk of skin cancer.