Abstract

In recent years, deep learning has gained significant traction in the fields of skincare and dermatology, with research primarily focused on two areas: cosmetic skin assessments and clinical diagnoses of skin diseases. Approximately two-thirds of the literature is dedicated to medical conditions such as melanoma and seborrheic keratosis, while the remaining third addresses cosmetic issues like UV damage, oiliness, pore size, and wrinkles. Although both domains utilize a similar methodological framework—training deep learning models on skin images for classification—they remain largely separate. Recent publications from 2024 and 2025 indicate a growing interest in integrating deep learning across these two fields. AI-driven skincare applications have shown promise in delivering rapid, personalized assessments, although challenges like biases in skin tone analysis continue to exist. In contrast, medical applications have made strides with deep learning models for skin cancer detection, achieving high accuracy and proving their potential as decision-support tools for dermatologists. The convergence of these domains presents a significant opportunity for hybrid models that could link cosmetic and medical applications, particularly for early skin cancer detection. By utilizing the extensive data and feature extraction techniques prevalent in skincare analysis, deep learning models could improve predictive accuracy and aid in the early identification of malignant conditions. This integration could facilitate proactive skin health monitoring by incorporating dermatological risk assessments into routine skincare evaluations. However, realizing this potential necessitates addressing challenges related to data diversity, model generalizability, and ethical considerations in AI-driven diagnostics. Bridging deep learning applications in skincare and dermatology not only offers the promise of improved early detection of serious skin conditions but also paves the way for more accessible, AI-powered dermatological care. Continued interdisciplinary collaboration is essential to develop these hybrid models and fully harness their potential in enhancing skin health diagnostics.