Modern healthcare faces a persistent challenge in delivering timely and accurate diagnostic decisions, particularly for complex diseases where clinical presentations vary widely across patients. Conventional systems, which rely on a single data source, often fail to capture the broader physiological narrative necessary for dependable clinical interpretation. To address this gap, this study introduces a unified multimodal intelligence framework that integrates three essential biomedical streams: laboratory-derived biomarkers, medical imaging modalities, and continuous physiological signals. The proposed system employs dedicated encoders for each modality and incorporates a fusion mechanism designed to preserve complementary diagnostic information while mitigating cross-modal inconsistencies. Experimental evaluation conducted on a multi-source clinical dataset demonstrates that the integrated model consistently outperforms its single-modality counterparts, yielding notable improvements in diagnostic accuracy, sensitivity, and early risk stratification. In addition to quantitative gains, the system provides clinically meaningful insights by highlighting cross-modal patterns linked to disease progression and patient-specific variations. The findings underscore the significant value of multimodal AI in enhancing clinical decision support, offering a more comprehensive and reliable diagnostic foundation. This work concludes that unified multimodal intelligence represents a promising direction for future precision medicine frameworks.

Keywords: Multimodal AI, Clinical Decision Support, Biomarkers, Medical Imaging, Physiological Signals, Data Fusion, Precision Medicine