Artificial Intelligence Application in Diagnosing, Classifying, Localizing, Detecting and Estimation the Severity of Skin Condition in Aesthetic Medicine: A Review

Abstract

Background

The integration of artificial intelligence (AI) and machine learning (ML) has revolutionized aesthetic medicine, enhancing the diagnosis, classification, and treatment of skin conditions. These technologies offer high precision, personalized care, and the potential to reduce human error. This review aimed to evaluate the current applications of AI and ML in aesthetic medicine, focusing on studies graded as Level I or II evidence by the Oxford Centre for Evidence-Based Medicine (CEBM).

Methods

A comprehensive search of MEDLINE, PubMed, and Ovid databases identified studies employing AI and ML for diagnosing and managing skin conditions. Studies were included if they demonstrated high diagnostic accuracy, improved treatment personalization, or other measurable clinical outcomes.

Results

AI and ML systems showed high accuracy in detecting and diagnosing conditions such as skin cancer, acne, psoriasis, and seborrheic dermatitis. AI-based platforms facilitated personalized treatment plans, enhancing therapeutic outcomes while minimizing errors. The integration of AI reduced diagnostic time and lowered healthcare costs, demonstrating significant potential for improving patient care. However, challenges such as algorithmic bias, data privacy concerns, and the need for high-quality training datasets were highlighted.

Conclusion

AI and ML have transformative potential in aesthetic medicine, offering improved diagnostic precision, enhanced patient outcomes, and cost reductions. Addressing limitations related to algorithm bias, regulatory oversight, and data quality is essential to fully realize the benefits of AI in clinical practice. Future research should focus on developing robust, ethical, and regulatory-compliant AI solutions.