JMIR Publications

ABSTRACT

Background:

Artificial intelligence (AI) continues to expand within dermatology, yet its role in nail disease remains relatively underrepresented compared to other areas of cutaneous imaging. Nail disorders often present with subtle or overlapping clinical findings, creating diagnostic difficulty even among experienced clinicians and contributing to delayed recognition of serious pathology such as subungual melanoma. As image-based AI systems become increasingly integrated into dermatologic research, understanding their potential applications and limitations in nail disease is becoming increasingly relevant.

Objective:

This scoping review evaluated current literature regarding AI-assisted diagnosis of nail disorders, with particular focus on image analysis systems, machine learning models, and emerging applications in melanonychia and subungual melanoma evaluation.

Methods:

A literature search was performed using PubMed and Embase with predefined search terms related to nail disease, melanoma, and artificial intelligence. Studies published within the last decade were screened for relevance and reviewed for diagnostic applications, model performance, and proposed clinical utility.

Results:

Current evidence suggests that AI-based systems demonstrate promising performance in the classification of nail disorders and may assist in narrowing diagnostically challenging presentations. Recent developments in deep learning, synthetic image augmentation, and federated learning frameworks also highlight the evolving adaptability of these technologies within limited dermatologic datasets. However, existing literature remains constrained by dataset heterogeneity, limited clinical validation, and lack of standardized implementation approaches.

Conclusions:

Overall, AI-assisted nail analysis represents a developing area of dermatologic innovation with potential future utility in clinical triage, diagnostic support, and earlier recognition of malignant nail disease.