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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.

This review discusses how emerging technologies in dermatology are enhancing the ability to diagnose and treat hair and nail disorders more accurately and effectively, highlighting advancements in both diagnostic techniques and therapeutic methods such as AI-driven technologies that personalize patient care and improve outcomes.

This study discusses the development of AI in dermatology and cosmetology, noting its use in skin cancer diagnosis and personalized cosmetics, while highlighting uncertainties about whether recent advancements represent significant medical progress or are influenced by marketing strategies.

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.

This study developed an AI-driven method for classifying cells in Follicular Lymphoma cases, achieving a 63% F1-score, precision, and recall in distinguishing centroblasts from other cell types using whole slide images at x20 resolution.