Artificial Intelligence-Driven Skin Aging Simulation as a Novel Skin Cancer Prevention

Introduction: Skin cancer, a prevalent cancer type among fair-skinned patients globally, poses a relevant public health concern due to rising incidence rates. Ultraviolet (UV) radiation poses a major risk factor for skin cancer. However, intentional tanning associated with sunburns remains a common practice, notably among female adults. Appropriate prevention campaigns targeting children and adolescents are needed to improve sun protection behavior particularly in these age groups. The aim of our study was to investigate if an AI-based simulation of facial skin aging can enhance sun protection behavior in female adults.

Methods: In this single-center, prospective, observational pilot study at Department of Dermatology at the University Hospital of Basel, we took photographs of healthy young females' faces with a VISIA-CR camera (Version 8.2; Canfield Scientific Inc., Parsippany, NJ, USA) between February and March 2021. Digital images were performed in three angles (straight, left 45°, and right 45°). All participants received an AI-based simulation of their facial skin with continuous aging to 80 years. A newly created anonymous questionnaire capturing participants' sociodemographic data and also tanning and sun protection behavior was completed in pre- and post-aging simulation. To observe long-term effects, a 2-year follow-up was conducted between March and April 2023.

Results: The 60 participants (mean age 23.6 ± 2.5 years) evaluated the importance of sun protection significantly higher after skin aging simulation with VISIA-CR camera (p < 0.0001; 95% CI: 8.2-8.8). Post-intervention, 91.7% (55/60) of the females were motivated to reduce UV exposure and to intensify UV protection in the future since the individual UV-dependent risk was perceived significantly higher (p < 0.001; 95% CI: 5.9-6.7). At 2-year follow-up, 96% (24/25) indicated persistent effort reducing UV exposure. The preference for SPF 50+ sunscreen increased to 46.7% (28/65) directly after the skin aging simulation and continued to rise up to 60.0% (15/25) after 2 years.

Conclusions: Our data emphasize the potential of AI-assisted photoaging interventions to enhance motivation for UV protection in the short and the long term. We encourage that different age and gender groups are addressed in a personalized, generation-specific manner with the appropriate media and by considering the Hawthorne effect. Campaigns with visual AI support can improve the intent of cancer-preventative behavior.