Integration of Radiology, Deep Learning, and Multimodal Health Data to Enhance Diagnostic Accuracy and Clinical Decision-Making

The diagnostic accuracy of radiology is often constrained when analyzed in isolation, missing critical clinical context available in electronic health records (EHRs), laboratory results, and genomic profiles. This study presents a multimodal AI framework designed to integrate radiological imaging with structured clinical data to enhance diagnostic accuracy and clinical decision-making. We propose a cross-modal attention-based fusion architecture that maps visual features from medical imaging to tabular clinical datasets, enabling a holistic patient assessment. Validated on a dataset of ۵,۰۰۰ cases, the model demonstrates a superior ability to differentiate complex pathologies by incorporating temporal and biological clinical parameters. Our results show that the integration of multimodal data improves overall diagnostic accuracy by [X]% compared to imaging-only models. This framework serves as a robust Clinical Decision Support System (CDSS), shifting the standard of care toward integrated, patient-centered, and evidence-based diagnostic workflows.

کلیدواژه ها:

Multimodal AI ، Clinical Decision Support Systems ، Data Fusion ، Electronic Health Records ، Diagnostic Accuracy ، Precision Medicine