Neuro-Fuzzy Architectures for Interpretable AI: A Comprehensive Survey and Research Outlook

(1) Background: The rapid rise of deep neural networks has highlighted the critical need for interpretable models, particularly in high-stakes domains such as healthcare, finance, and autonomous systems, where transparency and trustworthiness are paramount. Neuro-fuzzy systems, which combine the adaptive learning capabilities of neural networks with the interpretable reasoning of fuzzy logic, have emerged as a promising approach to address the explainability challenge in artificial intelligence (AI). (2) Methods: This paper provides an extensive survey of deep neuro-fuzzy architectures developed between 2020 and 2025, classifying them based on hybridization strategies, reviewing interpretability techniques, and analyzing their applications across diverse domains. We propose a standardized interpretability framework, an experimental setup using modern datasets, and a methodology for evaluating these systems. (3) Results: Recent architectures like DCNFIS, X-Fuzz, and PCNFI demonstrate exceptional performance and transparency in tasks such as image recognition, streaming data analysis, and biomedical diagnostics. We identify key challenges, including the interpretability-accuracy trade-off, scalability, and the lack of standardized metrics, while highlighting emerging trends such as neuro-symbolic integration and adversarial robustness. (4) Conclusions: Neuro-fuzzy systems are poised to become a cornerstone of trustworthy AI, but future research must address theoretical gaps, improve scalability, and establish standardized evaluation protocols to facilitate their widespread adoption in critical applications.