PathShield-Med: Interpretable Safety Control for Clinical Large Language Models through Internal Reasoning Path Intervention
Keywords:
clinical large language models, safety control, reasoning path intervention, interpretability, medical AI governance, mechanistic interventionAbstract
The integration of large language models into clinical decision-making has introduced transformative opportunities for diagnostic support, treatment planning, and patient communication. However, the high-stakes nature of medical practice demands exceptionally rigorous safety guarantees that surpass those required in general-domain applications. Existing safety mechanisms predominantly rely on external filtering, output moderation, or prompt-level constraints, which remain vulnerable to circumvention and lack the depth needed for clinical interpretability. This paper presents PathShield-Med, a novel framework for safety control in clinical large language models through targeted intervention upon internal reasoning pathways. Rather than treating the model as a black box, PathShield-Med identifies and modulates safety-critical reasoning trajectories within the model's forward computation, enabling precise, transparent, and auditable safety enforcement without sacrificing medical accuracy. The architecture integrates a reasoning path analyzer, a dynamic intervention engine, and a clinical knowledge verifier, all operating under a clinician-centered explainability layer that provides post-hoc and real-time justifications for every safety-relevant decision. Through extensive structural analysis, we examine the trade-offs between intervention granularity and clinical fluency, the governance implications of path-level control, and the infrastructure requirements for deploying such systems in regulated healthcare environments. We further discuss robustness under adversarial clinical queries, fairness across patient demographic representations, and alignment with evolving regulatory frameworks for artificial intelligence-based medical devices. PathShield-Med represents a shift from surface-level safety filters to deep, interpretable control embedded within the reasoning fabric of clinical language models.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
