Multimodal Artificial Intelligence for Early Disease Diagnosis and Clinical Decision Support using Medical Images and Patient Data

Abstract

The rapid growth of medical data generated from imaging systems, electronic health records, laboratory investigations, and patient monitoring platforms has created new opportunities for artificial intelligence-based clinical decision-support systems. Conventional diagnostic models generally rely on individual data modalities and may not represent the complete clinical reasoning process. This study proposes a multimodal artificial intelligence framework integrating medical images with structured patient information for improved early disease diagnosis and clinical decision support. The framework combines deep image feature extraction with clinical feature encoding followed by feature-level fusion. Comparative evaluation of image-only, clinical-data-only, and multimodal models demonstrates that integration of heterogeneous medical information improves diagnostic performance. The proposed framework shows potential for assisting clinicians through accurate, patient-specific and data-driven decision support