BACKGROUND: Dermatology is an ideal specialty for artificial intelligence (AI)-driven image recognition to improve diagnostic accuracy and patient care. Lack of dermatologists in many parts of the world and the high frequency of cutaneous disorders and malignancies highlight the increasing need for AI-aided diagnosis. Although AI-based applications for the identification of dermatological conditions are widely available, research assessing their reliability and accuracy is lacking.
OBJECTIVE: The aim of this study was to analyze the efficacy of the Aysa AI app as a preliminary diagnostic tool for various dermatological conditions in a semiurban town in India.
METHODS: This observational cross-sectional study included patients over the age of 2 years who visited the dermatology clinic. Images of lesions from individuals with various skin disorders were uploaded to the app after obtaining informed consent. The app was used to make a patient profile, identify lesion morphology, plot the location on a human model, and answer questions regarding duration and symptoms. The app presented eight differential diagnoses, which were compared with the clinical diagnosis. The model\'s performance was evaluated using sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and F1-score. Comparison of categorical variables was performed with the χ2 test and statistical significance was considered at P<.05.
RESULTS: A total of 700 patients were part of the study. A wide variety of skin conditions were grouped into 12 categories. The AI model had a mean top-1 sensitivity of 71% (95% CI 61.5%-74.3%), top-3 sensitivity of 86.1% (95% CI 83.4%-88.6%), and all-8 sensitivity of 95.1% (95% CI 93.3%-96.6%). The top-1 sensitivities for diagnosis of skin infestations, disorders of keratinization, other inflammatory conditions, and bacterial infections were 85.7%, 85.7%, 82.7%, and 81.8%, respectively. In the case of photodermatoses and malignant tumors, the top-1 sensitivities were 33.3% and 10%, respectively. Each category had a strong correlation between the clinical diagnosis and the probable diagnoses (P<.001).
CONCLUSIONS: The Aysa app showed promising results in identifying most dermatoses.

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Despite growing diversity in the United States population, studies show that medical education lacks representation of conditions in darker skin tones. Given that medical conditions present differently in different skin tones, limited exposure to images of darker tones in medical training may contribute to incorrect or delayed diagnoses, perpetuating health inequities. This study examines the preclinical curriculum at the Georgetown University School of Medicine (GUSOM) to report on its image representation with respect to skin tone and to assess the impact of a student-driven initiative in achieving visual learning equity (VLE). Of 1050 preclinical images, 58.2% depicted conditions in light/white skin tones, 31.3% in medium/brown, and 10.5% in dark/black. The microbiology and pathology courses had the highest percentages of dark/black and medium/brown images. Infectious disease images made up 36.3% of all images with 54.6% light/white, 31.5% medium/brown, and 13.9% dark/black. Overall, the first images representing conditions were 63.5% light/white, 30.0% medium/brown, and 6.6% dark/black. When dark/black images were presented first, 64.3% were of infectious diseases, compared to 35.1% for medium/brown and only 28.4% for white/light first images that were infectious diseases. A significant increase in images of conditions in darker skin tones was observed in the IRD course 2022 compared to the IRD course 2020 (P<.001). Our study highlights an underrepresentation of darker skin tones compared to lighter skin tones in the GUSOM preclinical curriculum. A student-led initiative significantly increased the representation of darker skin tones in dermatologic images, demonstrating the potential impact of such efforts in achieving VLE in medical education.J Drugs Dermatol. 2024;23(7):519-524.  doi:10.36849/JDD.7992.

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Geriatric in-patient dermatoses are diverse. Few data in Morocco describe the epidemiological profile and factors associated with average length of stay (LOS). Our aim was to identify these dermatoses and determine the factors associated with LOS.

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BACKGROUND: Prior research has explored the relationship between inflammatory skin disorders and breast cancer (BC), yet the causality of this association remains uncertain.
METHODS: Utilizing a bidirectional two-sample Mendelian randomization (MR) approach, this study aimed to elucidate the causal dynamics between various inflammatory skin conditions-namely acne, atopic dermatitis, psoriasis vulgaris, urticaria, and rosacea-and BC. Genetic variants implicated in these disorders were sourced from comprehensive genome-wide association studies representative of European ancestry. In the forward MR, BC was posited as the exposure, while the reverse MR treated each inflammatory skin disease as the exposure. A suite of analytical methodologies, including random effects inverse variance weighted (IVW), weighted median (WME), and MR-Egger, were employed to probe the causative links between inflammatory skin diseases and BC. Sensitivity analyses, alongside evaluations for heterogeneity and pleiotropy, were conducted to substantiate the findings.
RESULTS: The MR analysis revealed an increased risk of acne associated with BC (IVW: OR = 1.063, 95% CI = 1.011-1.117, p = 0.016), while noting a decreased risk of atopic dermatitis (AD) in BC patients (IVW: OR = 0.941, 95% CI = 0.886-0.999, p = 0.047). No significant associations were observed between BC and psoriasis vulgaris, urticaria, or rosacea. Conversely, reverse MR analyses detected no effect of BC on the incidence of inflammatory skin diseases. The absence of pleiotropy and the consistency of these outcomes strengthen the study\'s conclusions.
CONCLUSIONS: Findings indicate an elevated incidence of acne and a reduced incidence of AD in individuals with BC within the European population.

A recent study conducted in Khon Kaen Province, Thailand, evaluated the effectiveness of a technology-assisted intervention aimed at improving water quality and addressing related health issues in communities around key water bodies. The intervention targeted health concerns associated with water contamination, including chronic kidney diseases, skin conditions, hypertension, and neurological symptoms. The study included water quality assessments and health evaluations of 586 residents and implemented a Learning Innovation Platform (LIP) across 13 communities. Results showed significant improvements in the community, including a decrease in hypertension and skin-related health issues, as well as enhanced community awareness and proficiency in implementing simple water quality assessments and treatment. The study demonstrated the value of a comprehensive, technology-driven community approach, effectively enhancing water quality and health outcomes, and promoting greater community awareness and self-sufficiency in managing environmental health risks.

Peroxisome proliferator-activated receptor gamma (PPARγ) is a transcription factor expressed in many tissues, including skin, where it is essential for maintaining skin barrier permeability, regulating cell proliferation/differentiation, and modulating antioxidant and inflammatory responses upon ligand binding. Therefore, PPARγ activation has important implications for skin homeostasis. Over the past 20 years, with increasing interest in the role of PPARs in skin physiopathology, considerable effort has been devoted to the development of PPARγ ligands as a therapeutic option for skin inflammatory disorders. In addition, PPARγ also regulates sebocyte differentiation and lipid production, making it a potential target for inflammatory sebaceous disorders such as acne. A large number of studies suggest that PPARγ also acts as a skin tumor suppressor in both melanoma and non-melanoma skin cancers, but its role in tumorigenesis remains controversial. In this review, we have summarized the current state of research into the role of PPARγ in skin health and disease and how this may provide a starting point for the development of more potent and selective PPARγ ligands with a low toxicity profile, thereby reducing unwanted side effects.

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