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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 article reviews 25 years of hair restoration advancements, highlighting the shift towards techniques that emphasize naturalness and undetectability, and reports no new research findings.

This study describes the development of the BASP classification system, providing a universal and systematic approach for classifying pattern hair loss in both men and women.

This study found that faster algorithms for inferring ancestry in genomic data can better capture historical and functional insights into genome variation than traditional methods in large datasets like the UK Biobank.

This study found that while a modified BASP classification for pattern hair loss could classify previously unrecognized types, it was less accurate and harder to use than the existing BASP classification.