Open Access
ARTICLE
Multimodal Deep Learning Fusion of Medical Imaging and Electronic Health Records for Precision Disease Prediction
Issue Vol. 2 No. 01 (2026): Current Issue --- Section Articles --- Published Date: 2026-03-23
Abstract
Multimodal data integration has emerged as a critical approach for improving predictive performance in clinical decision-making systems. This study proposes a comprehensive deep learning framework that integrates medical imaging and electronic health records (EHRs) for precision disease prediction. The framework combines modality-specific encoders, including convolutional neural networks or Vision Transformers for imaging and transformer-based architectures such as BEHRT and Med-BERT for EHR data, with an attention-based fusion mechanism that captures cross-modal interactions. A hybrid fusion strategy is introduced to enable effective integration of heterogeneous representations while maintaining flexibility across different clinical tasks.
The model is evaluated on large-scale benchmark datasets, including MIMIC-CXR, CheXpert, and MIMIC-IV, using a multi-task learning setup for disease classification and risk prediction. Experimental results demonstrate that the proposed multimodal framework significantly outperforms unimodal baselines, achieving superior performance across accuracy, AUC, and F1-score metrics. Ablation studies further confirm the effectiveness of the hybrid fusion approach over traditional early and late fusion methods. In addition, the framework exhibits strong robustness to missing modalities and maintains consistent performance across varying dataset sizes, highlighting its scalability and practical applicability.
To enhance clinical interpretability, the model integrates Grad-CAM and SHAP for modality-specific explanations, providing insights into both imaging features and structured clinical variables. Furthermore, the framework supports privacy-preserving deployment through compatibility with federated learning and differential privacy techniques. These features position the proposed approach as a viable solution for real-world healthcare applications.
Overall, this study demonstrates that multimodal deep learning can significantly improve predictive accuracy and reliability in clinical settings, offering a scalable and interpretable framework for next-generation precision medicine systems.
