Title : Artificial intelligence in aesthetic dermatology: Assessing skin aging and monitoring cosmetic procedures
Abstract:
Introduction: Artificial intelligence (AI) is transforming aesthetic dermatology by providing objective tools for skin analysis. Deep learning algorithms and 2D/3D imaging platforms (VISIA®, Quantificare®, Antera 3D) enable quantification of wrinkles, pigmentation, texture, and elasticity, as well as simulation of cosmetic outcomes. Recent studies have shown AI achieving >90% accuracy in detecting changes after botulinum toxin injections and providing reliable metrics following radiofrequency (RF) and high-intensity focused ultrasound (HIFU) treatments. Despite its promise, challenges remain regarding data bias across skin phototypes, lack of standardized metrics, and implementation costs.
Objective: To review current applications of AI in aesthetic dermatology, focusing on skin aging assessment and monitoring of cosmetic procedures.
Methods: An integrative review was performed in PubMed, Scopus, and Web of Science, including articles published between 2020 and 2025 in English, Portuguese, and Spanish. Search terms included Artificial Intelligence, Aesthetic Dermatology, Skin Aging, Cosmetic Procedures, and Machine Learning. Editorials, duplicates, and studies unrelated to aesthetics were excluded.
Results: AI demonstrates effectiveness in:
- Objective assessment of cutaneous parameters (wrinkles, pigmentation, texture, firmness).
- Quantifying outcomes after botulinum toxin, fillers, and energy-based devices.
- Personalized cosmetology, suggesting skincare protocols based on digital profiling.
- Individualized treatment planning, enhancing patient safety and satisfaction.
However, large-scale validation and ethical integration are necessary, with emphasis on privacy and inclusivity of diverse phototypes.
Conclusion: AI is a promising tool to standardize and optimize aesthetic dermatology practice. Its integration may transform rejuvenation procedures into more precise and personalized interventions. Future efforts should prioritize international standardization, reduction of algorithmic bias, and regulatory frameworks ensuring safe implementation.
