Retrieval, not hallucinations, will be the limiting factor for LLM-based clinical AI tools
Kirk Roberts, Steven Bedrick, Kurt Miller, William R. Hersh, Hongfang Liu
cs.IR cs.CL
Abstract
Discussions around large language model (LLM) errors in clinical artificial intelligence (AI) generally center around precision errors like hallucinations. This perspective, targeting both clinicians and AI researchers, seeks to shift that discussion to recall errors, particularly in retrieval of patient-level data needed for many clinical AI tools. The perspective outlines types of errors and mitigation strategies, describes research directions in LLMs and retrieval, and provides an overview of retrieval evaluation.
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Classified with taxonomy v2 on Sat, 5 Sept 2026.