Artificial intelligence (AI) is increasingly being incorporated into dermatology, with hair disorders representing a particularly suitable field for its application. The assessment and management of alopecias rely heavily on visual evaluation, pattern recognition, longitudinal monitoring, and integration of heterogeneous clinical and biological data; processes that are often limited by interobserver variability and incomplete standardization. In recent years, AI-based approaches have been applied across multiple domains in the clinical assessment of hair disorders, including objective quantitative assessment, diagnostic support, personalized medicine, drug development, robotic-assisted procedures, and analysis of patient-reported outcomes and clinical communication. Among these applications, tools enabling automated and reproducible measurement of hair parameters are currently the most established in clinical practice, whereas diagnostic and predictive models remain largely investigational. This narrative review summarizes current and emerging applications of artificial intelligence in hair disorders, with a focus on their clinical relevance and practical implementation. In addition, methodological limitations, ethical considerations, and challenges related to external validation, interpretability, and data bias are discussed. Although AI-based tools are not yet able to replace clinical judgement, they hold significant potential to complement dermatologists’ expertise and improve objectivity, efficiency, and personalization in the management of hair disorders.
Lecumberri et al. (Fri,) studied this question.