Large Language Models Are Poor Medical Coders — Benchmarking of Medical Code Querying

Abstract

BACKGROUND Large language models (LLMs) have attracted significant interest for automated clinical coding. However, early data show that LLMs are highly error-prone when mapping medical codes. We sought to quantify and benchmark LLM medical code querying errors across several available LLMs.