MedFusion-Mamba: Redefining Clinical AI with Segmentation-Guided Multimodal Deep Learning for Superior Disease Prediction

Accurate and reliable disease prediction in clinical settings requires models that can adapt to heterogeneous data sources and maintain robustness across diverse environments. The proposed framework, MedFusion-Mamba, introduces a hybrid deep learning approach that integrates foundation model-based anatomical segmentation, self-supervised visual feature extraction, state-space sequence modeling, and tabular EHR fusion into a unified architecture. The design enables enhanced focus on relevant anatomical structures, improved generalization with minimal labeled data, and effective exploitation of temporal or volumetric imaging information. The integration of structured clinical data further strengthens predictive capabilities, while adaptive mechanisms at inference time ensure resilience against domain shifts. Evaluations target multi-label thoracic disease prediction and multimodal clinical outcome forecasting, emphasizing both performance accuracy and interpretability. The architecture aims to advance predictive healthcare by offering a robust, efficient, and transparent solution adaptable to diverse clinical contexts