Can you imagine anticipating the struggle between the immune system and a tumor using mathematical tools that understand each patient's uncertainty? Researchers at ESPOL developed an innovative model based on Type-3 Fuzzy Logic that simulates precisely that confrontation. Unlike traditional models, this proposal integrates individual biological variability and delays in the immune response, allowing highly accurate prediction of chaotic patterns and possible tumor relapses. In addition, the study generates clear and interpretable risk maps that facilitate the design of personalized and explainable therapies, aligning with the principles of Explainable Artificial Intelligence (XAI) to support clinical decision-making.