Intelligent Skincare: AI-Driven Cosmetic Product Recommendation Using Advanced Machine Learning Models - Nguyen, An Thi Phuong; Akter, Arjina (American Journal of Health, Medicine and Nursing Practice, 2025)
Purpose: This research presents an advanced AI-powered system designed to revolutionize cosmetic product selection by providing highly personalized recommendations based on individual skin characteristics. Materials and Methods: Unlike traditional methods relying on beauty consultants or algorithm-based digital platforms, the proposed system leverages deep learning models to enhance precision, efficiency, and reliability. Findings: At its core, the system employs state-of-the-art image recognition models, including Convolutional Neural Networks (CNN) with 92% accuracy, VGGNet at 94%, and DenseNet achieving 98%, to analyze user-uploaded skin images and identify dermatological concerns such as dryness, oiliness, and red spots. By integrating sophisticated AI methodologies, the system generates scientifically informed recommendations across skincare, makeup, and haircare categories. Implications to Theory, Practice and Policy: This study bridges the gap between AI and personalized skincare, offering an innovative, data-driven approach that enhances the consumer experience with evidence-based, highly tailored cosmetic guidance. Through AI-driven dermatological analysis, this research contributes to the advancement of intelligent skincare solutions, ensuring precise, effective, and scientifically backed product recommendations.