Artificial intelligence in microscopic hair imaging for scalp disorders: From image acquisition to clinical decisions

Medical imaging plays a central role in modern clinical decision-making by transforming raw image data into actionable diagnostic insights. In the context of scalp and hair disorders, microscopic hair imaging has emerged as a critical tool for non-invasive, repeatable, and cost-effective evaluation. This review provides the first comprehensive and systematic overview of the application of artificial intelligence in microscopic hair imaging. We propose a novel five-module framework that integrates imaging acquisition, representation transfer, structural analysis, generative restoration and clinical decision-making. This framework delineates the causal pathway from pixel-level operations to clinical readouts, addressing challenges such as resolution sensitivity, noise interference, and optical acquisition parameters. The review emphasizes the importance of establishing task-specific benchmarks for the accurate and reproducible assessment of hair-related clinical goals in scalp disorders. By decoupling methodology from specific tasks, this review uncovers generalizable principles that can be applied across various datasets and clinical objectives. As the first review of its kind, this work aims to inspire future research and development in AI-powered hair diagnostics, advancing personalized treatment and improving clinical practice.