Retrieval, not hallucinations, will be the limiting factor for LLM-based clinical AI tools
For clinicians and AI researchers, it reframes the key challenge in clinical LLM applications from precision to recall, but is a perspective piece without empirical results.
The paper argues that recall errors in patient data retrieval, not hallucinations, will be the primary limitation for LLM-based clinical AI tools, and outlines error types, mitigation strategies, and research directions.
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.