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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.