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Profiling Hallucinations in Frontier LLMs for Entity Linking to Medical Ontologies

The integration of Large Language Models (LLMs) into healthcare promises to revolutionize clinical documentation and interoperability, yet reliability remains a concern. This study presents a comprehensive analysis of hallucinations by frontier LLMs tasked with mapping clinical text to SNOMED CT. Through rigorous experimentation, we identify a critical reliability gap: LLMs hallucinate medical codes at a rate that currently renders them unsuitable for autonomous clinical coding applications. Paradoxically, constraining models to use ground-truth mention spans exacerbates, rather than mitigates, these hallucinations. We further contribute a taxonomy of hallucination types – including deprecated codes and cross-ontology errors – and demonstrate that general-purpose LLMs significantly underperform compared to specialized zero-shot entity linking approaches. These findings underscore the need for robust verification mechanisms before clinical deployment.