Graph-Enhanced Clinical Knowledge Injection for Secure and Robust Medical LLM Reasoning - Tobias D. Greene; Zixuanan Wan; Ananya M. Chopra; Kiran Chatterjee (Computational Intelligence Systems, 2026)
Large language models have demonstrated remarkable capabilities in natural language understanding and generation, yet their deployment in high-stakes medical contexts remains fraught with challenges related to factual accuracy, adversarial vulnerability, and systemic bias. This paper proposes a graph-enhanced clinical knowledge injection framework that systematically integrates structured biomedical ontologies and relational knowledge graphs into the reasoning pipeline of medical large language models. By embedding graph-based representations of clinical entities, relationships, and hierarchical dependencies, the proposed architecture augments model outputs with verifiable domain constraints and causal pathways, thereby improving both security and robustness. We examine the architectural trade-offs between expressivity and computational overhead, the role of graph neural network layers in preserving semantic integrity, and the implications for adversarial robustness when knowledge graphs serve as external verifiers. The framework is situated within a broader governance perspective that addresses data provenance, fairness across demographic groups, and sustainability of large-scale inference. Through cross-domain comparisons with existing retrieval-augmented generation and fine-tuning approaches, we highlight the structural advantages of graph-enhanced injection for mitigating hallucinations and resisting malicious perturbations. The paper concludes with a forward-looking discussion on the deployment of such systems in clinical decision support, the need for continuous validation against evolving medical knowledge, and the policy infrastructure required to ensure equitable access and accountability.