Scalp Disorder Imaging: How Deep Learning and Explainable Artificial Intelligence are Revolutionizing Diagnosis and Treatment

Deep learning is transforming the diagnosis of scalp disorders by automating the classification of image data, but challenges remain in model transparency and reliability. This review highlights the role of explainable AI (XAI) in improving the interpretability of these models, helping to identify biases and understand decision-making processes. Despite promising results, issues like data quality and model interpretability need addressing. Future research should enhance real-time detection and severity prediction capabilities, improve data diversity, and ensure model generalizability across populations. XAI is crucial for building trust in AI-driven scalp disorder diagnosis and promoting its clinical adoption.