🔍 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.