Can AI Overcome Subjectivity in Aesthetic Dermatology Evaluations?
🔍 Key Finding
AI shows promise in providing objective evaluations in aesthetic dermatology but is hindered by biased datasets, inconsistent evaluation methods, and a lack of standardized assessment protocols.
🔬 Methodology Overview
Design: Comprehensive review of AI applications in dermatology, focusing on diagnostic and aesthetic fields.
Data Sources: Analysis of traditional methods (subjective surveys, hardware devices) and emerging AI technologies.
Evaluation: Comparison of AI’s potential against limitations such as dataset bias, subjective assessments, and hardware variability.
📊 Evidence
AI achieved dermatologist-level accuracy in skin cancer detection (AUROC 0.86 in melanoma recognition studies).
Current aesthetic evaluation tools (e.g., Baumann Skin Type Indicator, imaging devices like Canfield VISIA) lack standardization and struggle with ethnic/age diversity.
AI-based mobile apps matched expert performance in diagnosing pigmented lesions but lagged in treatment decision-making.
💡 Clinical Impact
AI could enhance objectivity in aesthetic assessments, improve treatment personalization, and support monitoring over time. However, reliance on non-standardized tools risks biased outcomes and limits comparability across practices.
🤔 Limitations
No universal standards for aesthetic evaluations (unlike EASI/PASI in medical dermatology).
Training datasets lack diversity in skin types, ages, and ethnicities.
High costs of AI-integrated devices may exacerbate healthcare disparities.
Ethical concerns (data privacy, over-reliance on AI) remain unresolved.
Thunga S, Khan M, Cho SI, Na JI, Yoo J. AI in Aesthetic/Cosmetic Dermatology: Current and Future. Journal of Cosmetic Dermatology. 2025;24:e16640. https://doi.org/10.1111/jocd.16640