Artificial Intelligence (AI) has revolutionized healthcare diagnostics by enabling automated analysis of complex medical datasets, particularly through deep learning models capable of achieving high predictive accuracy in medical imaging and disease classification tasks. Despite their success, deep neural networks often operate as black-box systems, limiting interpretability, transparency, and clinician trust in high-stakes medical decision-making environments. The lack of explainability raises ethical, regulatory, and accountability concerns, especially in critical diagnostic applications. This study proposes a conceptual Hybrid Deep Learning–Fuzzy framework designed to enhance explainability, uncertainty management, and transparency in AI-driven medical diagnosis systems. The primary objective is to integrate the powerful feature extraction capabilities of convolutional neural networks (CNNs) with the human-like reasoning characteristics of fuzzy inference systems (FIS) to create a balanced and interpretable diagnostic architecture. The proposed methodology consists of a multi-layered structure in which deep learning extracts high-dimensional clinical features, which are subsequently translated into linguistic variables and rule-based decisions through fuzzy logic mechanisms. The framework theoretically improves interpretability by generating rule-supported explanations and confidence measures alongside diagnostic predictions. Comparative analysis with traditional deep learning approaches suggests that the hybrid model maintains predictive robustness while significantly enhancing transparency and handling uncertainty in medical data. The findings indicate that combining neural computation with fuzzy reasoning can bridge the gap between performance and explainability, thereby improving clinical trust and ethical compliance. The study concludes that hybrid Deep Learning–Fuzzy architectures represent a promising direction for responsible and explainable AI deployment in healthcare, offering substantial potential for future real-world validation and scalable medical decision-support systems.