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.

Background: Symmetrical drug‐related intertriginous and flexural exanthema (SDRIFE) is a rare hypersensitivity reaction characterized by symmetric erythematous rashes in intertriginous regions, triggered by systemic drugs. Although self‐limiting, SDRIFE can significantly impact quality of life, requiring accurate diagnosis and management. Objectives: This study aimed to describe the clinical characteristics, diagnostic approaches, and implicated pharmacological agents in the largest reported SDRIFE series, providing insights to improve diagnosis and treatment. Materials and Methods: A retrospective observational study was conducted between 2018 and 2024. Data on demographics, clinical features, implicated drugs, and diagnostic tests—including drug provocation tests (DPTs), lymphocyte transformation tests (LTTs), and intradermal tests—were analyzed. Results: Among 41 patients, the mean age was 66.6 years, with frequent polypharmacy complicating diagnosis. Beta‐lactam antibiotics and iodinated contrast agents were the most implicated drugs. Nine previously unreported drugs were identified as triggers. DPT confirmed causality in six cases, and LTT aided in three complex cases. Avoidance of the suspected drug or pharmacological group was the main therapeutic measure. Conclusions: Systematic medication reviews and standardized protocols are crucial for SDRIFE diagnosis. DPT remains the gold standard, while LTT adds value in complex cases, avoiding unnecessary drug restrictions and expanding therapeutic options.