This review outlines current treatments for folliculitis decalvans and dissecting cellulitis of the scalp, noting conventional therapies' limitations and promising but limited results from targeted biologic agents.
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October 2024 in “BMC Medical Informatics and Decision Making” This study used machine learning models to analyze PCOS symptoms for early diagnosis, finding Support Vector Machine and VGG16 algorithms achieved high accuracy rates of 94.44% and 98.29% respectively.
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March 2024 in “arXiv (Cornell University)” This study describes an AI-powered system for diagnosing dermatological conditions, achieving a weighted score of 0.87 in both contextual understanding and diagnostic accuracy, suggesting it could enhance tele-dermatology applications by supporting remote consultations and care access in underserved regions.
January 2026 in “Open Science Framework” This scoping review describes the current use of artificial intelligence in alopecia research, highlighting AI's evolution from diagnostic to prognostic applications in dermatology and identifying gaps in multimodal integration and fairness across demographics.
In this study, researchers explored the diverse causes of alopecia in dogs, highlighting how factors such as infection, hormonal imbalances, and genetic conditions contribute to hair loss, and emphasized the importance of tailored diagnostics and treatment strategies.