Cognitive Hazard Mapping

Medical LLMs are increasingly used for diagnosis support, triage, documentation, and patient interaction, yet traditional validation methods often fail to detect unsafe reasoning hidden behind fluent clinical language. This chapter introduces Cognitive Hazard Mapping, a framework designed to identify and prevent reasoning-level risks such as diagnostic hallucination, premature closure, causal misalignment, and guideline divergence. By analyzing inference pathways, applying multi-layer checks, and integrating clinician review, the model uncovers subtle hazards that may lead to patient harm despite seemingly accurate outputs. The chapter presents a taxonomy of cognitive hazards, real-world case studies, and methods for monitoring, auditing, and aligning LLM behavior with regulatory expectations. Cognitive Hazard Mapping provides a foundation for safer, more transparent, and clinically trustworthy AI systems.

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