CareGuardAI: Context-Aware Multi-Agent Guardrails for Clinical Safety & Hallucination Mitigation in Patient-Facing LLMs

CareGuardAI is a safety framework designed to mitigate clinical risks and hallucinations in patient-facing medical LLMs through dual risk assessment mechanisms. The system employs context-aware multi-agent guardrails that evaluate both clinical safety and factual reliability before releasing responses, outperforming GPT-4o-mini on specialized healthcare benchmarks.

CareGuardAI addresses a critical gap in healthcare AI deployment: the tension between LLM capability and clinical safety. While large language models excel at generating plausible text, they often fail to reject unsafe medical advice or acknowledge knowledge limitations—a dangerous flaw when patients rely on them for health information. This research tackles two distinct failure modes: responses that are factually correct but clinically inappropriate given patient context, and hallucinations that present false medical information with confidence.

The framework's innovation lies in its inference-time approach rather than relying solely on training-based safety measures. By implementing a controller agent that assesses both clinical safety risk (SRA) and hallucination risk (HRA) before responding, CareGuardAI creates a gating mechanism preventing release of substandard outputs. The methodology draws from established medical safety standards (ISO 14971), grounding the approach in healthcare compliance frameworks rather than generic AI safety principles.

For healthcare organizations and AI developers, this represents a pragmatic pathway toward regulatory compliance and patient safety without sacrificing model capability. The framework demonstrates that runtime safety mechanisms can be as important as model architecture in high-stakes domains. Benchmarking against specialized medical safety datasets rather than general-purpose evaluation sets provides stronger evidence of real-world applicability.

The bounded-latency guarantee and consistent outperformance over GPT-4o-mini suggest CareGuardAI could accelerate adoption of LLMs in clinical decision support, patient education, and telemedicine platforms. Future work will likely explore integration with electronic health record systems and real-time patient monitoring data.

• →CareGuardAI implements dual-risk assessment (clinical safety and hallucination detection) at inference time using context-aware multi-agent evaluation.
• →The framework outperforms GPT-4o-mini on PatientSafeBench, MedSafetyBench, and MedHallu benchmarks, demonstrating superior performance on healthcare-specific safety metrics.
• →Context-aware guardrails address LLM limitations in interpreting patient information and challenging unsafe assumptions that clinicians would flag.
• →The system only releases responses meeting both SRA and HRA thresholds ≤2, ensuring clinically acceptable quality with bounded computational latency.
• →Runtime safety mechanisms prove critical for healthcare deployment, complementing training-based safety and regulatory compliance standards.