Trichoscopy and Computational Models for Hair and Scalp Disorders: Image Analysis, Quantification, and Clinical Integration

This scoping review highlights the current state of computational image analysis and AI in assessing hair and scalp disorders, focusing on quantitative trichoscopy and operator-independent evaluation. It reveals that few studies integrate algorithms into real-world clinical pathways, with most literature being pre-clinical or methodology-focused. Commercial AI-assisted trichoscopy platforms lack peer-reviewed evidence, with proprietary validation methods and datasets hindering independent verification. Academic applications primarily use convolutional neural networks for follicular unit quantification. While AI-assisted trichoscopy could standardize quantitative outputs, its clinical translation is limited by small datasets, varied protocols, and a lack of external validation and prospective studies.