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The integration of artificial intelligence into healthcare systems demands frameworks that ensure trustworthiness, particularly in safety-critical bedside reasoning scenarios. This conceptual manuscript introduces the knowledge distillation and compression network (KDCN), a novel representation compression framework designed to distill complex clinical knowledge graphs into compact, interpretable structures suitable for real-time inference at the point of care. By leveraging graph compression techniques, the KDCN aims to mitigate risks associated with opaque AI decision-making in clinical workflows, enhancing interoperability across electronic health record (EHR) ecosystems and decision support pipelines. The framework incorporates layered governance mechanisms to monitor inference integrity, promoting safety in high-stakes environments like intensive care units. Theoretical analysis explores how representation compression reduces computational overhead while preserving semantic fidelity in clinical knowledge representations. We synthesize literature on clinical AI architectures, healthcare analytics infrastructures, and AI governance systems to contextualize the KDCN’s contributions. Conceptual formulas model risk propagation through compressed graphs and decision confidence thresholds, underscoring the framework’s potential to foster trustworthy AI deployment. This work advances conceptual systems research by proposing infrastructural innovations for safer, more efficient bedside reasoning without relying on empirical evaluations or datasets. Ultimately, the KDCN offers a pathway toward resilient AI integration in healthcare, balancing efficiency with ethical imperatives for patient safety.