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In the evolving landscape of artificial intelligence integration within healthcare, ensuring the consistency between radiology reports and corresponding images emerges as a critical safety signal to mitigate diagnostic errors and enhance patient outcomes. This conceptual manuscript proposes a novel verification framework designed to systematically assess report–image agreement, framing it as an essential mechanism for quality assurance in radiology workflows. Drawing from theoretical foundations in AI trustworthiness and medical imaging informatics, the framework delineates an architectural infrastructure that orchestrates multi-layered verification processes, incorporating governance protocols to detect discrepancies in impressions derived from radiological data. By conceptualizing agreement as a dynamic safety indicator, the system addresses potential risks such as interpretive drift and resource misallocation through interpretive formulas that model risk propagation, decision confidence, and monitoring burden. The architecture emphasizes a unique feedback topology to enable iterative refinement without relying on empirical data or performance metrics. This approach fosters a theoretical basis for deploying AI-assisted tools in clinical environments, highlighting infrastructural considerations for scalability and ethical integration. Ultimately, the framework contributes to the discourse on safe AI applications in radiology by prioritizing consistency verification as a proactive safeguard, potentially reducing adverse events and supporting informed clinical decision-making in diverse healthcare settings.