Applications and prospects of artificial intelligence in the auxiliary diagnosis of nail diseases

Diagnosis of nail disorders often relies on the expertise of experienced clinicians. However, artificial intelligence (AI), which has achieved significant progress in the field of diagnosis of skin diseases in recent years, also holds great promise for the identification and auxiliary diagnosis of nail diseases. This review summarizes the imaging features of nail disorders, such as changes in color and morphology, and the corresponding requirements they pose for image processing algorithms. This paper also reviews examples of AI model applications in the auxiliary diagnosis of common nail diseases. For onychomycosis, multiple studies have developed various AI models based on different image types. In the case of nail psoriasis, existing AI models have mainly focused on automating severity scoring systems. For melanonychia, relevant AI models typically identify disease patterns through segmentation and/or classification approaches. In addition to nail diseases, AI models can also assist in diagnosing systemic conditions such as diabetes by analyzing microvascular changes and hemoglobin distribution in nail images. Although current models have their limitations, the continued accumulation of high-quality datasets, ongoing algorithmic advancements, and the development of standardized clinical applications are expected to make AI an indispensable tool in the auxiliary diagnosis of nail diseases.