Search

The integration of multi-modal data sources in healthcare represents a pivotal advancement for enhancing diagnostic precision and clinical decision-making. This conceptual manuscript proposes a novel architectural framework, termed the diagnostic fusion intelligence lattice (DFIL), designed to orchestrate the seamless fusion of imaging modalities—such as MRI, CT, and X-ray—with structured clinical data from electronic health records (EHRs). By emphasizing interoperability, governance, and workflow integration, DFIL addresses the challenges of data heterogeneity, diagnostic latency, and human-AI collaboration in clinical environments. The framework incorporates layered structures for data ingestion, fusion orchestration, and decision augmentation, incorporating feedback topologies to mitigate diagnostic drift and ensure ethical oversight. Theoretical analyses explore operational dynamics, including risk propagation models and governance sensitivities, without empirical validation. Drawing on recent literature in clinical AI architectures and healthcare analytics, this work synthesizes insights into how such systems could transform diagnostic pipelines in settings like oncology, neurology, and cardiology. Key contributions include conceptual formulas for fusion confidence and resource allocation, highlighting trade-offs in multi-modal integration. Ultimately, DFIL offers a blueprint for future AI-driven diagnostic ecosystems, promoting safer, more efficient healthcare delivery through theoretical infrastructural innovation.

Polypharmacy, defined as the concurrent use of five or more medications, is highly prevalent among older adults and patients with multiple chronic conditions and is associated with an increased risk of drug–drug interactions (DDIs), leading to adverse drug events, hospitalizations, and higher healthcare costs. Existing DDI databases are often incomplete and fail to capture higher-order interactions, while many machine learning approaches overlook temporal prescription patterns and molecular structure information, limiting their effectiveness in real-world clinical settings. To address these limitations, this study proposes a graph neural network (GNN)-based framework that integrates prescription sequence data with molecular representations to improve DDI prediction. The model constructs a unified graph where drug nodes encode both known interactions and learned similarities, while a prescription sequence encoder captures temporal co-prescribing patterns and a molecular encoder processes SMILES-based structures. These multimodal representations are fused within a patient–drug interaction graph and refined using GNN layers with attention mechanisms to enhance interpretability. By combining longitudinal clinical data with chemical structure information, the framework enables more accurate, context-aware, and patient-specific prediction of DDIs, supports the identification of novel interactions, and improves risk stratification in polypharmacy settings, offering a scalable and interpretable foundation for future clinical decision support systems.