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In this study, a modified Xception deep learning model achieved a 92% accuracy rate in diagnosing hair and scalp disorders, significantly outperforming other models, suggesting AI could improve dermatological diagnostics' accuracy and accessibility.

This study explored peripheral blood immune dysregulation in alopecia areata through single-cell analyses, identifying systemic changes linked to disease severity and key signaling roles for monocytes, NK cells, and memory T cells, suggesting potential therapeutic targets.

This study analyzed gene expression data to identify key genes involved in severe forms of alopecia areata, discovering four immune monitoring genes (LGR5, SHISA2, HOXC13, S100A3) with potential for early diagnosis and better understanding of the disease's biological mechanisms.

In this study, the BaldGraphFormer framework, integrating visual and clinical data, outperformed unimodal baselines in early-stage androgenetic alopecia detection, achieving an F1-score of 97.62% and macro-average AUC of 0.992, suggesting its potential to support dermatological decision-making and early intervention.

This study introduced the KRDQN predictive framework, which outperformed existing methods in predicting adverse drug reactions and provided interpretable insights into drug mechanisms, aiding pharmacovigilance and clinical decision-making.