PathGuard-Med: Interpretable Safety Alignment for Clinical Large Language Models via Multi-Hop Reasoning Intervention
Keywords:
clinical large language models, interpretable safety alignment, multi-hop reasoning, intervention architecture, healthcare AI governanceAbstract
The deployment of large language models in clinical settings promises transformative improvements in diagnostic support, patient communication, and administrative efficiency, yet it simultaneously introduces profound risks related to patient safety, interpretability, and regulatory compliance. Existing safety alignment techniques, such as reinforcement learning from human feedback and constitutional AI, primarily operate at the output level, penalizing harmful responses without providing transparent mechanisms for why a given response was deemed unsafe or how it could be corrected. This paper introduces PathGuard-Med, a novel interpretable safety alignment framework designed specifically for clinical large language models. PathGuard-Med leverages multi-hop reasoning intervention to trace and adjust the internal reasoning pathways of a model before a response is generated, thereby enabling clinicians and compliance officers to inspect, validate, and override safety decisions in real time. The framework integrates a multi-hop graph structure that encodes clinical knowledge, ethical constraints, and regulatory guidelines into interleaved reasoning chains. When a query enters the system, PathGuard-Med routes the computation through a series of intervention points where explicit reasoning steps are monitored and, if necessary, redirected to avoid unsafe conclusions. This architecture does not merely filter outputs but restructures the model’s reasoning process, making safety alignment both interpretable and auditable. We discuss the structural trade-offs between intervention granularity and computational overhead, the governance challenges of deploying such a system in hospital networks, and the policy implications for algorithmic accountability under frameworks like the FDA’s Software as a Medical Device guidelines. Through a comparative analysis with existing approaches, we demonstrate that PathGuard-Med achieves higher transparency without sacrificing clinical utility. The paper concludes with forward-looking perspectives on embedding interpretable safety mechanisms into next-generation clinical AI infrastructures.
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