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ISACA Advanced in AI Security Management (AAISM) · Isaca
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Hallucination solution is always to have human in the loop to verify.
Addresses the root cause — ensures real-world data is accurate/complete/reliable before it influences model outputs, directly reducing hallucination risk at the source
When deploying an AI solution to generate treatment regimens—a high-risk, safety-critical use case—the most effective way to reduce the risk of hallucinations is to ensure that a qualified human expert reviews, approves, or overrides AI outputs before they influence patient care. A human-in-the-loop (HITL) mitigates hallucination risks by: Catching incorrect, implausible, or clinically unsafe recommendations Ensuring AI outputs align with medical standards, guidelines, and clinical judgment Providing accountability and oversight, especially when real-world data introduces noise, bias, or edge cases This is strongly aligned with FDA, EMA, and ISO/IEC 42001 expectations for high-risk AI.