Introduction: Botulinum toxin is widely used to treat upper facial wrinkles, and its efficacy is typically assessed through photographic comparisons and standardized scales. Artificial intelligence (AI) is increasingly being integrated into aesthetic dermatology for objective wrinkle evaluation. Objectives: This study aimed to compare human and AI-based assessments of pre- and posttreatment of upper facial wrinkles and evaluate their consistency and treatment effectiveness. Methods: A total of 228 individuals (204 females, 24 males) who received abobotulinum toxin for glabellar, forehead, and lateral canthal wrinkles were analyzed using pre- and posttreatment photographs. Wrinkles were assessed by four human raters using the 5-point Merz scale and Global Aesthetic Improvement Scale (GAIS). AI evaluations were conducted using Haut.AI Face Skin Metrics 2.0, a pre-trained machine learning platform. Results: AI had better error rates than humans for age prediction. The AI and human assessments showed high agreement for static and dynamic wrinkle evaluations (P<0.001). Posttreatment analysis indicated significant wrinkle reduction in both the human and AI assessments (P<0.001). Human assessment of GAIS scores was negatively correlated with wrinkle reduction (P<0.001). The treatment effects measured by AI and human raters showed a weak-to-moderate correlation. Conclusion: AI-based assessments align well with human evaluations and can detect posttreatment improvements. However, the treatment effect did not correlate well with human evaluations. AI can serve as an objective tool for evaluating botulinum toxin treatment outcomes and complementing human assessments. However, there is still a need for a gold standard method to evaluate aesthetic improvement and harmony.
Küçük et al. (2026) studied this question.