Enhanced Medical Diagnosis Using Multimodal Deep Learning: A Comprehensive approach to Data Fusion and Analysis

By using Multimodal Deep Learning, various data sets can be used to improve accuracy and reliability in medical diagnosis. In this context, we present MedFusionNet, a new deep learning architecture designed to integrate imaging data such as MRIs and CT scans with electronic health records for optimized EHR-driven clinical decision support systems. The primary objective of MedFusionNet is to address the limitations imposed by single-modality models using a primary hybrid fusion approach which employs CNNs, RNNs, and transformers to perform feature complementarity extraction and alignment from various data sources. Evaluation experiments indicated that MedFusionNet has achieved better results compared to preceding models with respect to staging Alzheimer’s disease, detecting pulmonary embolisms and other hallmark tasks. The diagnosis of these conditions was accomplished with high rates of accuracy, robust tolerance for missing data, and low tendency towards overfitting. Attention must be given to the innovative dynamic real-time diagnosis and treatment planning underpinned by MedFusionNet. Future work will focus on model validation and incorporating interpretability within clinical settings.