Generative AI-Based Virtual Hand Skin Tone and Nail Art Color Harmony: An Integrative Review Toward Personalized Nail Color Selection Guidelines

Nail art has evolved from a decorative beauty practice into a personalized cosmetic service that reflects individual image, style, and self-expression. Nevertheless, nail color selection remains largely dependent on personal preference, fashion trends, social-media images, and practitioner experience rather than standardized color-harmony criteria. This integrative review examines how objective skin color science, nail art design characteristics, personal color concepts, and generative artificial intelligence (AI) can be combined to establish a methodological basis for personalized nail color selection. The literature on human skin color measurement, CIELAB colorimetry, color-difference analysis, personal color and nail color matching, virtual beauty technologies, and skin-tone bias in AI-generated imagery was synthesized. The evidence supports representing hand skin color as a multidimensional profile based on L*, a*, b*, chroma, and hue angle rather than relying solely on warm/cool or seasonal categories. Nail art should likewise be described using colorimetric, optical, reflective, decorative, and compositional features. Generative AI offers a scalable means of creating standardized virtual hand–nail stimuli, but current evidence demonstrates substantial skin-tone imbalance and potential color inaccuracy in generated images. We therefore propose a six-stage framework: hand skin-color profiling, nail-art feature classification, generative AI-based virtual image generation, colorimetric accuracy and bias validation, skin–nail harmony modeling, and personalized guideline development. This framework should be regarded as hypothesis-generating rather than as a validated recommendation system. External validation using objectively measured human hand skin color and perceptual evaluation is required before clinical, commercial, or professional implementation.