Explainable Multi-Modal AI Framework for Early Disease Diagnosis in Smart Healthcare Systems

Abstract

Early and accurate disease diagnosis requires the integration of multiple data sources along with transparent decision-making mechanisms. This paper presents an Explainable Multi-Modal AI Framework for Early Disease Diagnosis in Smart Healthcare Systems. The proposed system combines heterogeneous data modalities, including medical images, electronic health records, and clinical parameters, to improve diagnostic performance. Deep learning architectures such as Convolutional Neural Networks and Transformer-based models are employed for feature extraction and representation learning. A multi-modal fusion strategy is applied to integrate diverse data sources, enabling comprehensive analysis of patient information. To address the black-box nature of deep learning, explainable AI techniques such as SHAP and attention visualization are incorporated to provide interpretable insights into model predictions. The framework enhances clinician trust by offering transparency and justification for decisions. Experimental results demonstrate improved accuracy, reliability, and interpretability compared to conventional models. Additionally, integration with prior research in healthcare analytics strengthens system robustness and generalization. The study highlights the importance of combining multi-modal learning with explainability to develop trustworthy and efficient clinical decision support systems.