AI-Driven Analysis of UV-Induced DNA Damage and Melanoma Risk Assessment

Ultraviolet (UV) radiations are considered to be an important contributor to the incidence of melanoma due to the ability of this radiations to induce damage at the DNA level. While current systems emphasize a large understanding of the processes due to UV radiationsinduced damage, it remains unknown in a large context due to lack of the involvement of interactions between the various environmental factors and their correlations with repair mechanisms as well as correlations between these mechanisms with changes in skin pigmentation. The complexity of the processes involved here remains so unknown that melanoma could hardly be prevented through a perfect strategy. This spectrum of UV-induced DNA damage is unraveled by an AI-designed system that considers environmental data, repair mechanisms, and interactions with skin pigments. Its goal lies in improving early detection and melanoma prevention through a multidimensional understanding of UV-induced genetic alterations. The proposed system integrates AI algorithms for data fusion, real-time environmental condition monitoring, and personalized risk assessment based on skin pigmentation. The benefits include more accurate predictions of risk, improved strategies for prevention, and tailored management of the risk of melanoma according to an individual's skin type.