NEUROFUSION: A UNIFIED AI MODEL FOR MULTI-MODAL HEALTHCARE DATA ANALYSIS

Abstract

Modern healthcare generates vast quantities of heterogeneous data from multiple sources including medical imaging, electronic health records, genomic sequences, and wearable sensor streams. However, most artificial intelligence systems analyze these modalities in isolation, missing critical cross-modal relationships that could improve diagnostic accuracy and treatment planning. This research presents NeuroFusion, a unified deep learning architecture that integrates and analyzes multi-modal healthcare data through shared latent representations and cross-attention mechanisms. The model employs modality-specific encoders processing medical images, clinical text, time-series physiological signals, and genomic data, which feed into a fusion layer learning complementary information across modalities. Evaluated on three clinical datasets encompassing cardiovascular disease diagnosis, cancer prognosis, and ICU mortality prediction, NeuroFusion achieves 94.2% accuracy in cardiovascular classification, 89.7% in cancer outcome prediction, and 91.3% in mortality forecasting—representing 6-8% improvements over single-modality baselines and 3-5% gains compared to simple concatenation approaches. The model's attention mechanisms provide interpretable insights into which data modalities contribute most to specific predictions, addressing the black-box criticism of deep learning in clinical contexts. However, challenges persist including computational requirements demanding 32GB GPU memory for training, data alignment complexity across modalities with different temporal resolutions, and limited generalization when deployed on institutions with different data collection protocols. Privacy considerations necessitate federated learning approaches when training across multiple hospitals. Despite these limitations, NeuroFusion demonstrates that unified multi-modal architectures can unlock synergistic information from diverse healthcare data sources, paving the way toward more comprehensive AI-assisted clinical decision support systems.