AI-Driven Approaches for Dynamic Melanoma Progression Analysis

Melanoma, a deadly form of skin cancer, accounts for approximately 60,000 deaths annually worldwide and demands advanced diagnostic and predictive methods to improve patient outcomes and reduce unnecessary biopsies. Recent advancements in Artificial Intelligence (AI), including Generative Adversarial Networks (GANs), hybrid models, and Deep Learning (DL) architectures, have achieved diagnostic accuracies ranging from 90% to 96% in state-of-the-art studies, showcasing significant potential in addressing challenges related to early detection and progression prediction. This research reviews the recent studies focusing on AI techniques for melanoma analysis, discussing their contributions and relevance to a dynamic melanoma progression framework. The key findings of this research reveal that GAN-based methods improve class balance and enhance model training by generating up to 20% more diverse synthetic data, while DL-based models consistently outperform traditional methods in lesion classification. The research synthesizes these insights to propose their integration into a personalized Digital Twin (DT) framework for lesion progression analysis. Additionally, it highlights challenges such as imbalanced datasets in up to 70% of reviewed cases, lack of dynamic simulation models, and the need for personalized treatment optimization while outlining opportunities for future integration of advanced AI methods.