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This study reports that an Explainable AI-based hair health prediction system using a novel hybrid model outperformed traditional machine learning methods, achieving high accuracy in predicting key factors and providing personalized recommendations.

This review reports that while deep learning shows promise in diagnosing scalp disorders from images, challenges remain with data quality and model interpretability, suggesting that integrating explainable AI techniques is crucial for building trust and facilitating clinical adoption.

This study identified a four-gene loop as a non-invasive biomarker that selectively activates in alopecia areata, providing a precise target for JAK inhibitor treatments.