Concept of Hierarchically Organized Explainable Intelligent Systems: Synthesis of Deep Neural Networks, Fuzzy Logic and Incremental Learning in Medical Diagnostics

The development of explainable artificial intelligence (XAI) goes beyond traditional post-hoc methods of interpretation, forming a new generation of cognitive-oriented intelligent systems based on deep integration of neural network methods, fuzzy logic and incremental learning. The report presents the concept of hierarchically organized XAI systems that provide adaptive explainability and continuous knowledge updating in the process of their practical application. The implementation is based on the dynamic formation of explanations, including a cascade connection of local and global interpreters, production models and adaptive mechanisms for generating medical reports. The presented approach forms the XAI 2.0 paradigm, providing resistance to uncertainty of medical data, cognitive transparency of decisions and the possibility of multi-level interpretation of results. The paper discusses the theoretical and practical principles of building such systems, and substantiates the need to integrate deep neural networks and fuzzy logic to create intelligent medical systems of a new generation.