Neurofuzzy systems in health: preventive tools and clinical support

Abstract

Hybrid neurofuzzy systems combine two approaches in artificial intelligence: neural networks, which learn patterns from examples to improve decision-making accuracy, and fuzzy logic, which enables reasoning with gradual categories such as “low,” “medium,” and “high.” When integrated, these approaches achieve a valuable balance between performance and interpretability. These models not only learn from real data but also generate recommendations that are understandable to both users and professionals. In the clinical field, their role is not to diagnose but to support, prioritize, and educate. This article explains, in an accessible manner, how these systems work and the benefits they offer to patients, families, and healthcare teams. It presents practical examples, such as estimating alert levels for individuals at risk of developing type 2 diabetes based on habits and simple measurements. It also describes how these models can produce precise and transparent action pathways, supported by interpretable metrics, which help anticipate potential health problems through the analysis of clinical data. In conclusion, when designed with ethical criteria—privacy, non-discrimination, and transparency—and validated within the contexts in which they will be applied, neurofuzzy systems can become valuable allies for prevention and service organization, bringing AI closer to communities through a responsible and humanistic approach.