Hybrid Deep Learning Framework for Interpretable Healthcare Diagnostics Integrating Multi-Modal Data for Enhanced Trust and Accuracy

Abstract

The growing use of artificial intelligence (AI) within healthcare demands models that boast both high-performance and interpretability. This study presents a hybrid deep learning framework that combines multi-modal data for precise disease predictions along with actionable and interpretable insights, which in turn can drastically enhance the quality of diagnosis. Through the integration of CNN and Transformer-based models, along with advanced feature fusion techniques, the comprehensive framework guarantees those whose predictive performance is optimal across a wide range of datasets. Additionally, employ explainability modules like Grad-CAM, SHAP which allows users to see why the model made a certain prediction with visualizations in a more interpretable manner like heatmaps or feature importance scores, thus increasing trust in the model. Experiments on public datasets (e.g., MIMIC-IV, ChestX-ray8, and COVID-19 CT) show better accuracy and higher explainability than traditional black-box models. This study forges a vital connection in the space of healthcare AI, stressing the importance of performance along with transparency to support the ethical and effective implementation of AI systems in the clinical environment.