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In the realm of healthcare analytics, sparse and irregular longitudinal health records pose significant challenges to traditional representation models, often treating missing data as mere artifacts to be imputed or discarded. This conceptual manuscript proposes a paradigm shift by framing missingness itself as an informative signal within a representation theory tailored for electronic health records (EHRs). We introduce the irregular signal encoding architecture (ISEA), a theoretical framework that integrates missingness patterns into core data representations, enhancing clinical decision support without empirical imputation. Drawing from clinical AI architectures and healthcare analytics infrastructures, ISEA comprises layered modules for signal extraction, temporal irregularity mapping, and sparsity-aware integration, fostering interoperability across EHR ecosystems. Theoretically, this approach mitigates biases in decision pipelines by leveraging missingness as a proxy for unobserved clinical dynamics, such as patient non-adherence or resource constraints. We outline governance mechanisms to monitor representation fidelity and discuss infrastructural implications for deployment in heterogeneous health systems. Formulas for decision confidence and risk propagation underscore the interpretive value of missingness, promoting robust AI governance. This theory advances EHR intelligence by reconceptualizing data voids as actionable insights, paving the way for more resilient healthcare analytics without relying on simulated experiments or performance metrics.

In the evolving landscape of artificial intelligence integration within healthcare systems, ensuring diagnostic reliability in radiology reports remains a paramount challenge. This conceptual manuscript introduces the semantic coherence diagnostic reliability (SCDR) framework, a novel architectural model designed to enhance consistency as a core quality metric in radiology diagnostics. By focusing on semantic coherence, the framework addresses discrepancies in report generation that arise from heterogeneous data sources, algorithmic biases, and workflow variabilities. Drawing from clinical AI architectures, healthcare analytics infrastructures, and decision support pipelines, the SCDR Framework proposes a layered structure incorporating semantic alignment modules, coherence monitoring loops, and reliability governance protocols. Theoretical analysis explores how this framework mitigates diagnostic drift through interpretive formulas for risk propagation and decision confidence. Without empirical evaluations, the discussion emphasizes infrastructural implications for interoperability in electronic health record (EHR) ecosystems and AI deployment systems. The framework’s unique feedback topology fosters adaptive coherence in multi-modal radiology data, promoting enhanced diagnostic trustworthiness. Ultimately, this work advocates for semantic coherence as a foundational metric in AI-driven radiology, offering pathways for improved clinical workflow integration and governance in diagnostic environments.