Paper: AI Gains Ground in Cosmetic Dermatology Workflows

Key Takeaways

• Artificial intelligence (AI) is increasingly integrated into aesthetic dermatology, supporting diagnosis, treatment planning, and patient education.
• Deep learning models demonstrate strong performance in classifying conditions such as acne, vitiligo, and pigmentary disorders, often approaching dermatologist-level accuracy in controlled settings.
• Despite rapid progress, limitations in data diversity, model generalizability, and regulatory standards remain barriers to widespread clinical adoption.

A recent review in Dermatology and Therapy assessed the growing use of artificial intelligence (AI) across aesthetic dermatology, with the authors reporting increased adoption and highlighting its use in many aspects of practice.

"We have endeavored to comprehensively present the applications of AI in aesthetic dermatology, covering areas such as detection, diagnosis, treatment assistance, and patient education," they wrote in the paper. "Overall, the healthcare space has seen the most focus in two main scenarios: AI as a decision-support tool for clinicians and its role in directly enhancing patient-facing medical services."

Aesthetic dermatology, they wrote, has traditionally relied on subjective clinical assessment in evaluating skin quality, lesion severity, and treatment outcomes. AI-driven tools are now being used to quantify features such as hydration, pigmentation, and skin thickness using clinical and consumer-grade imaging. These systems may reduce reliance on semi-quantitative grading scales and improve reproducibility.

In diagnostics, AI models have demonstrated have shown high performance; deep learning approaches have achieved strong sensitivity and segmentation accuracy in vitiligo assessment, while classification models for benign pigmented lesions and melasma have reported 90%+ accuracy ratings in controlled datasets. Acne grading systems using smartphone-based imaging have also shown high concordance with dermatologists. Beyond diagnosis, AI is being explored for treatment planning, optimizing phototherapy parameters, predicting filler injection volumes, and automating hair density measurements for transplantation planning. Emerging systems also incorporate real-time safety monitoring.

The authors were careful to note that challenges still persist. The present analysis was limited by small or homogeneous datasets, lack of standardized evaluation frameworks, and concerns regarding explainability and data security.

“AI is unlikely to supplant dermatologists, but rather supplement their work,” the authors wrote. “The adoption of technology could improve clinical pathways… and potentially transform the future of our healthcare system.”

Source: Chen X, et al. Dermatol Ther (Heidelb). 2025;15:1999-2013. Doi:10.1007/s13555-025-01459-2