Deciphering Chaos: A new “Fuzzy” Artificial Intelligence to predict the battle between the immune system and cancer
https://www.eurekalert.org/news-releases/1114243
Publish Date: 2026-01-27 12:16:00
Source Domain: www.eurekalert.org
-
New Hybrid Model for Tumor-Immune Interaction Analysis: Researchers from ESPOL have developed a computational framework combining Type-3 Fuzzy Logic and neural networks to simulate interactions between tumors and the immune system.
-
Incorporating Biological Uncertainty: The model addresses variability in patient immune responses and the latency in cytotoxic T-cell activation, generating “bands of uncertainty” to visualize multiple potential treatment outcomes.
-
Interpretable and Logic-Oriented: Unlike traditional AI, the new approach maintains interpretability, preserving chaotic structures and bifurcations, and allowing physicians to understand the rationale behind predictions.
-
Visual Clinical Risk Maps: The model generates risk maps based on linguistic rules, classifying patients into safe or danger zones and guiding personalized treatment strategies.
-
Superior to Conventional Techniques: The hybrid model outperforms current methods by accurately capturing oscillatory behaviors in cancers even with incomplete or noisy data.
-
Support for Precision Oncology: By using Explainable Artificial Intelligence (XAI), the model transforms complex data into clear, actionable diagnoses, supporting decision-making in precision oncology.
