CareGuardAI: Context-Aware Multi-Agent Guardrails for Clinical Safety & Hallucination Mitigation in Patient-Facing LLMs
Integrating large language models (LLMs) into patient-facing healthcare systems offers significant potential to improve access to medical information. However, ensuring clinical safety and factual reliability remains a critical challenge. In practice, AI-generated responses may be conditionally correct yet medically inappropriate, as models often fail to interpret patient context and tend to produce agreeable responses rather than challenge unsafe assumptions. Unlike clinicians, who infer risk from incomplete information, LLMs frequently lack contextual awareness. Moreover, real-world patient interactions are open-ended and underspecified, unlike structured benchmark settings. We present ...
| Authors | Elham Nasarian, Abhilash Neog, Kwok-Leung Tsui et al. |
|---|---|
| Primary category | cs.CY |
| Published | 7 April 2026 |
| Last updated at source | 7 April 2026 |
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