Explainable Deep Learning Models for Precision Healthcare: Bridging AI Interpretability and Clinical Trust

Abstract

Recent advancements in artificial intelligence have significantly reshaped the landscape of precision healthcare, enabling automated diagnostic systems, predictive analytics, and treatment recommendations. However, the adoption of deep learning models in clinical environments remains limited due to their black-box nature and lack of interpretability. This paper proposes an explainable deep learning framework designed to enhance both diagnostic performance and clinical transparency. The framework integrates convolutional neural networks (CNNs) for feature extraction and transformer-based attention mechanisms for contextual reasoning, augmented by an interpretability module that generates visual saliency maps and textual rationales to bridge human-AI understanding. Experimental results on multiple medical imaging datasets-including chest X-rays, retinal scans, and histopathology slides-demonstrate superior performance in both accuracy and interpretability metrics compared to conventional models. By quantifying feature importance and visual attribution, the proposed model establishes a transparent decision-making process that aligns closely with clinician reasoning, thus fostering trust in AI-assisted healthcare systems. The study highlights that interpretability not only enhances model accountability but also accelerates clinical adoption of deep learning technologies for precision medicine.