AI in Aesthetic/Cosmetic Dermatology: Current and Future

Abstract

Background: Recent advancements in artificial intelligence (AI) have significantly impacted dermatology, particularly in diagnosing skin diseases. However, aesthetic dermatology faces unique challenges due to subjective evaluations and the lack of standardized assessment methods.

Aims: This review aims to explore the current state of AI in dermatology, evaluate its application in diagnosing skin conditions, and discuss the limitations of traditional evaluation methods in aesthetic dermatology. Additionally, the review proposes strategies for future integration of AI to address existing challenges.

Methods: A comprehensive review of AI applications in dermatology was conducted, in both diagnostic and aesthetic fields. Traditional methods such as subjective surveys and hardware devices were analyzed and compared with emerging AI technologies. The limitations of current AI models were evaluated, and the need for standardized evaluation methods and diverse datasets was identified.

Results: AI has shown great potential in diagnosing skin diseases, particularly skin cancer. However, in aesthetic dermatology, traditional methods remain subjective and lack standardization, therefore limiting their effectiveness. Emerging AI applications in this field show promise, but they have significant limitations due to biased datasets and inconsistent evaluation methods.

Conclusions: To develop the potential of AI in aesthetic dermatology, it is crucial to create standardized evaluation methods, collect diverse datasets reflecting various ethnicities and ages, and educate practitioners on AI's utility and limitations. Addressing these challenges will improve diagnostic accuracy, better patient outcomes, and help integrate AI effectively into clinical practice.

Keywords: aesthetic; cosmetic dermatology; dermatology; digital imaging.