🔍 Key Findings

Machine learning and AI applications in cosmetic dermatology are showing promising results across five main areas: product development, skin assessment, condition diagnosis, treatment recommendations, and outcome prediction.

🔬 Methodology

• Systematic review following PRISMA protocol

• Search across SCOPUS, IEEE Xplore, PubMed (2018-2023)

• Query: terms combining dermatology/skin, cosmetic, and AI/ML

• Selection: Full research papers in English focusing on cosmetic dermatology using AI

• Three-stage screening: title, abstract, full text

• Final sample: 63 papers categorized into 5 domains (product development, assessment, diagnosis, treatment recommendation, outcome prediction)

📊 Evidence

• 63 relevant papers published between 2018-2023 were analyzed

• Studies showed accuracy rates ranging from 75.4% to 98% across different applications

• Most successful applications were in skin classification (ResNets achieving 98% accuracy) and condition diagnosis (DenseNet121 achieving 94.4% accuracy for melasma)

💡 Clinical Impact:

AI tools can assist dermatologists in:

• Automating routine skin assessments

• Providing more objective severity scoring

• Improving diagnostic accuracy

• Offering personalized treatment recommendations

• Predicting treatment outcomes

🤔 Limitations

• Most studies had small, specific datasets

• Lack of diverse skin types in training data

• Limited real-world validation

• Most AI models lack explainability ("black box" problem)

• Many studies focused on single conditions rather than multiple concurrent conditions