Multi-Modal AI Integration for Comprehensive Patient Risk Assessment: Combining Clinical, Imaging, and Genomic Data

Abstract

The accurate and timely assessment of patient risk remains one of the most critical challenges in modern medicine, influencing early detection, preventive intervention, and personalized treatment planning. Traditional risk prediction models often rely on a single type of data. These most commonly structured clinical records limit their ability to capture the complex and multifaceted nature of disease progression. Emerging evidence suggests that integrating heterogeneous data sources, such as structured electronic health records (EHR), high-resolution medical imaging, and genomic profiles, can offer complementary insights that significantly enhance predictive accuracy. This paper presents a comprehensive multi-modal artificial intelligence (AI) framework that combines these three data modalities using a late-interaction transformer-based fusion architecture deployed over a hybrid cloud Integration Platform-as-a-Service (iPaaS). The proposed system is designed to process a diverse range of formats, including HL7/FHIR clinical data, DICOM imaging studies, and VCF genomic variant files, thereby harmonizing them within a secure, governed, and scalable analytics environment. The research employs modality-specific encoders for tabular, imaging, and genomic data, which are pre-trained using self-supervised techniques to maximize information retention, even in low-label environments. A cross-attentional fusion mechanism is employed to align latent representations from each modality, with uncertainty-aware gating to ensure robust performance in the presence of incomplete data. The predictive component integrates survival analysis objectives with classification-based risk scoring, enabling both short- and long-term prognostic modeling. The system is operationalized using a hybrid cloud deployment model that leverages on-premises resources for sensitive workloads while utilizing elastic cloud infrastructure for computationally intensive AI training. This approach results in a projected $20M cost optimization over three years and a 50% faster integration delivery cycle compared to legacy pipelines.

The framework was evaluated across multi-center cohorts for cardiovascular and oncology risk assessment. Results demonstrate a 35% improvement in predictive accuracy compared to the best-performing unimodal baseline. In cardiovascular prediction tasks, the concordance index (C-index) improved from 0.74 to 0.80, while Brier scores showed an 18% reduction, reflecting enhanced calibration. In oncology, the C-index improved from 0.69 to 0.75 for predicting progression-free survival. The model’s interpretability framework provided clinically meaningful explanations, linking radiographic features, laboratory results, and polygenic risk scores to risk stratification outcomes. The deployment leveraged reusable iPaaS connectors for FHIR, DICOM, and genomic pipelines, ensuring compliance with HIPAA and GDPR standards while enabling interoperability across hospital systems. This research bridges the gap between algorithmic innovation and healthcare system integration, demonstrating how multi-modal AI can be securely, efficiently, and effectively embedded into enterprise healthcare environments. Beyond improved prediction accuracy, the proposed solution delivers operational scalability, cost efficiency, and regulatory compliance, making it a viable blueprint for large-scale clinical adoption. The findings affirm that combining clinical, imaging, and genomic data via multi-modal AI not only elevates predictive performance but also aligns with the operational realities of modern healthcare IT ecosystems