Explainable Deep Learning Architecture for Transparent and Trustworthy Medical Diagnostics

Abstract

This paper introduces an explainable deep learning architecture designed to improve transparency, interpretability, and trust in AI-based medical diagnostics. The model combines attention-guided convolutional networks with a rule-based interpretive layer that generates human-readable clinical reasoning behind each prediction. By integrating saliency mapping, feature attribution, and semantic explanation modules, the system supports clinicians in validating diagnostic outcomes. Evaluations conducted on multi-modal datasets, including radiology images and electronic health records, show enhanced diagnostic accuracy while maintaining clear interpretability. The work contributes a practical and reliable pathway for deploying AI in sensitive healthcare workflows where accountability and explainability are critical.