This scoping review summarizes current computational image analysis and artificial intelligence (AI) approaches for the assessment of hair and scalp disorders, with emphasis on quantitative trichoscopy and operator-independent evaluation. A deep Medline search was performed using a citation network-based approach using MeSH terms and complementary keywords covering diagnostic imaging, trichoscopy/videodermoscopy, image processing, algorithms, AI, and mobile/smartphone-based workflows. Overall, relatively few studies assess algorithms in real-world clinical pathways, and much of the retrieved literature is predominantly pre-clinical or methodology-driven. In parallel, commercially available AI-assisted trichoscopy platforms have little or no traceable peer-reviewed evidence; their validation methods and underlying datasets are often proprietary, undisclosed, and not directly comparable, limiting independent verification and cross-platform benchmarking. The most mature academic applications focus on follicular unit quantification (hair density, shaft diameter distribution, vellus-to-terminal ratio, and severity mapping), mainly using convolutional neural networks with object detection and instance segmentation. In conclusion, AI-assisted trichoscopy may support a shift toward standardized quantitative outputs, but clinical translation remains early and constrained by small or proprietary datasets, heterogeneous acquisition/annotation protocols, limited external validation, and scarce prospective studies.
Zengarini et al. (Thu,) studied this question.