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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.