1 citations
,
January 2023 in “IEEE access” This review examines advancements in deep learning methods for detecting dermatological conditions from dermoscopic images, summarizing available datasets and suggesting future research directions, but reports no new results.
December 2023 in “International Journal of Dermatology” This study found an increased risk of asthma and allergic rhinitis in individuals with hidradenitis suppurativa.
This study reviewed the use of self-supervised Auto ML models for detecting alopecia areata, finding significant advancements in automated diagnosis but also challenges such as model explainability and data bias, which may guide future AI-driven dermatological diagnostics.
July 2025 in “Harvard Dataverse” A deep learning model accurately detects early hair loss signs using scalp images.
7 citations
,
July 2019 in “Advances in preventive medicine” Among 255 subjects with diabetes, this study found an 88.4% prevalence of skin conditions and 15.7% prevalence of nail manifestations, linked with factors like age and disease duration.
