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Background: There is literature that suggests metformin may play a protective role against the development of non-melanoma skin cancers. Given the significant burden of disease non-melanoma skin cancers represent, the possibility of a widely available and generally well-tolerated medication such as metformin as part of the prevention and treatment ladder warrants further research., Objective: This study aims to evaluate the potential of metformin in reducing the risk of non-melanoma skin cancers, specifically squamous cell carcinoma and basal cell carcinoma, using the All of Us research database., Methods: A retrospective case-control analysis was conducted using the All of Us database. Propensity score matching and multivariable regression analyses were performed to evaluate the impact of metformin on the incidence of squamous cell carcinoma (SCC) and basal cell carcinoma (BCC) while controlling for confounding variables., Results: Our results indicate a reduced risk of non-melanoma skin cancer following exposure to metformin in individuals diagnosed with both SCC and BCC. Subgroup analyses revealed that metformin exposure was associated with a decreased risk of BCC across all sex and ethnicity groups. Metformin use was also associated with a significantly lower risk of SCC, with univariable and multivariable ORs consistently showing reduced odds. However, metformin exposure was not significantly associated with decreased SCC risk in African American patients., Conclusion: Our study's findings indicate a potential protective effect of metformin against skin cancer, particularly in patients with skin of color. Further prospective research is necessary to substantiate metformin's role in skin cancer chemoprevention within these populations.J Drugs Dermatol. 2024;23(12):1089-1095. doi:10.36849/JDD.8249.

Background: 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)., Objective: 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)., Materials and Methods: 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., Results: 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., Conclusion: The findings of this review suggest promising potential for AI to enhance the accuracy and efficiency of Mohs surgery, particularly for NMSC., (Copyright © 2024 by the American Society for Dermatologic Surgery, Inc. Published by Wolters Kluwer Health, Inc. All rights reserved.)

Introduction: Indoor tanning is a major modifiable risk factor in the development of both melanoma and nonmelanoma skin cancers. Investigation of behavior-altering interventions is an area of active research. As with other preventive measures, screening of high-risk populations can be an important aspect of a multimodality public health intervention. This study sought to further the limited understanding of indoor tanning screening practices in the primary care setting., Methods: Physicians practicing within the scope of primary care in the northeast were surveyed in 2022 on practice patterns around the frequency of indoor tanning screening, barriers encountered with implementing screening, and actions taken with a positive screen. Research methodology adhered to the Joanna Briggs Institute critical appraisal checklist., Results: Of 26 primary care physicians, only 7.7% routinely screened for indoor tanning. Barriers identified included time limitations (76.9%) and prioritization of other health concerns (96.2%). All primary care physicians (100%) reacted to reports of indoor tanning with an intervention, most commonly counseling on the risks of indoor tanning (92.6%)., Conclusions: This data suggest that screening for indoor tanning use could be improved. The authors recommend the incorporation of a standardized screening question regarding indoor tanning in intake forms., (© 2024 The Author(s).)

Despite the importance of informed consent in healthcare, the readability and specificity of consent forms often impede patients' comprehension. This study investigates the use of GPT-4 to simplify surgical consent forms and introduces an AI-human expert collaborative approach to validate content appropriateness. Consent forms from multiple institutions were assessed for readability and simplified using GPT-4, with pre- and post-simplification readability metrics compared using nonparametric tests. Independent reviews by medical authors and a malpractice defense attorney were conducted. Finally, GPT-4's potential for generating de novo procedure-specific consent forms was assessed, with forms evaluated using a validated 8-item rubric and expert subspecialty surgeon review. Analysis of 15 academic medical centers' consent forms revealed significant reductions in average reading time, word rarity, and passive sentence frequency (all P < 0.05) following GPT-4-faciliated simplification. Readability improved from an average college freshman to an 8th-grade level (P = 0.004), matching the average American's reading level. Medical and legal sufficiency consistency was confirmed. GPT-4 generated procedure-specific consent forms for five varied surgical procedures at an average 6th-grade reading level. These forms received perfect scores on a standardized consent form rubric and withstood scrutiny upon expert subspeciality surgeon review. This study demonstrates the first AI-human expert collaboration to enhance surgical consent forms, significantly improving readability without sacrificing clinical detail. Our framework could be extended to other patient communication materials, emphasizing clear communication and mitigating disparities related to health literacy barriers., (© 2024. The Author(s).)

Importance: The progression of artificial intelligence (AI) text-to-image generators raises concerns of perpetuating societal biases, including profession-based stereotypes., Objective: To gauge the demographic accuracy of surgeon representation by 3 prominent AI text-to-image models compared to real-world attending surgeons and trainees., Design, Setting, and Participants: The study used a cross-sectional design, assessing the latest release of 3 leading publicly available AI text-to-image generators. Seven independent reviewers categorized AI-produced images. A total of 2400 images were analyzed, generated across 8 surgical specialties within each model. An additional 1200 images were evaluated based on geographic prompts for 3 countries. The study was conducted in May 2023. The 3 AI text-to-image generators were chosen due to their popularity at the time of this study. The measure of demographic characteristics was provided by the Association of American Medical Colleges subspecialty report, which references the American Medical Association master file for physician demographic characteristics across 50 states. Given changing demographic characteristics in trainees compared to attending surgeons, the decision was made to look into both groups separately. Race (non-White, defined as any race other than non-Hispanic White, and White) and gender (female and male) were assessed to evaluate known societal biases., Exposures: Images were generated using a prompt template, "a photo of the face of a [blank]", with the blank replaced by a surgical specialty. Geographic-based prompting was evaluated by specifying the most populous countries on 3 continents (the US, Nigeria, and China)., Main Outcomes and Measures: The study compared representation of female and non-White surgeons in each model with real demographic data using χ2, Fisher exact, and proportion tests., Results: There was a significantly higher mean representation of female (35.8% vs 14.7%; P < .001) and non-White (37.4% vs 22.8%; P < .001) surgeons among trainees than attending surgeons. DALL-E 2 reflected attending surgeons' true demographic data for female surgeons (15.9% vs 14.7%; P = .39) and non-White surgeons (22.6% vs 22.8%; P = .92) but underestimated trainees' representation for both female (15.9% vs 35.8%; P < .001) and non-White (22.6% vs 37.4%; P < .001) surgeons. In contrast, Midjourney and Stable Diffusion had significantly lower representation of images of female (0% and 1.8%, respectively; P < .001) and non-White (0.5% and 0.6%, respectively; P < .001) surgeons than DALL-E 2 or true demographic data. Geographic-based prompting increased non-White surgeon representation but did not alter female representation for all models in prompts specifying Nigeria and China., Conclusion and Relevance: In this study, 2 leading publicly available text-to-image generators amplified societal biases, depicting over 98% surgeons as White and male. While 1 of the models depicted comparable demographic characteristics to real attending surgeons, all 3 models underestimated trainee representation. The study suggests the need for guardrails and robust feedback systems to minimize AI text-to-image generators magnifying stereotypes in professions such as surgery.

Competing Interests: None disclosed.