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

The document reviews the integration of AI and computational models in trichoscopy for diagnosing hair and scalp disorders, emphasizing the transition from manual to automated systems that improve accuracy and reproducibility. AI models, particularly deep learning, have shown high accuracy in diagnosing conditions like alopecia, with some achieving up to 98% accuracy. Despite these advancements, challenges such as limited datasets, proprietary data, and lack of standardized protocols hinder clinical translation and real-world application. The review underscores the potential of AI to standardize diagnostics in trichology and highlights the need for future research to focus on standardized imaging, transparent data reporting, and shared benchmarking frameworks to enhance clinical integration and validation.