Reimagining nail health: The next frontier in dermatologic innovation

Nail disorders are a frequently overlooked aspect of dermatology despite their effects on function, appearance, and QOL. Although artificial intelligence (AI)-driven skin analysis tools have advanced rapidly for acne, pigmentation, lesion classification, and melanoma risk assessment, the nail unit remains underrepresented in validated digital dermatology platforms. This gap limits early diagnosis, objective monitoring, and treatment assessment for conditions including onychomycosis, nail psoriasis, longitudinal melanonychia, and traumatic nail dystrophies. This review examines current AI-enabled dermatology platforms, identifies gaps in nail assessment, and proposes a framework for future nail-focused computational imaging. Published studies and publicly available platforms were evaluated for imaging methods, analytic capabilities, validation, and clinical relevance. Skin-focused AI systems demonstrate image analysis techniques applicable to nail disease, yet most consumer nail applications remain cosmetic. Emerging studies support the feasibility of AI-assisted nail imaging, but they are limited by small datasets, retrospective designs, limited external validation, and underrepresentation of diverse skin tones and nail morphologies. Standardized nail photography, expert-annotated datasets, explainable AI, prospective validation, and teledermatology integration are needed to translate AI-assisted nail assessment into clinically useful tools and advance precision digital dermatology.