CheX-GPT: Harnessing Large Language Models for Enhanced Chest X-ray
  Report Labeling
Jawook Gu Han-Cheol Cho Jiho Kim Kihyun You Eun Kyoung Hong Byungseok Roh
Free-text radiology reports present a rich data source for various medical
tasks, but effectively labeling these texts remains challenging. Traditional
rule-based labeling methods fall short of capturing the nuances of diverse
free-text patterns. Moreover, models using expert-annotated data are limited by
data scarcity and pre-defined classes, impacting their performance, flexibility
and scalability. To address these issues, our study offers three main
contributions: 1) We demonstrate the potential of GPT as an adept labeler using
carefully designed prompts. 2) Utilizing only the data labeled by GPT, we
trained a BERT-based labeler, CheX-GPT, which operates faster and more
efficiently than its GPT counterpart. 3) To benchmark labeler performance, we
introduced a publicly available expert-annotated test set, MIMIC-500,
comprising 500 cases from the MIMIC validation set. Our findings demonstrate
that CheX-GPT not only excels in labeling accuracy over existing models, but
also showcases superior efficiency, flexibility, and scalability, supported by
our introduction of the MIMIC-500 dataset for robust benchmarking. Code and
models are available at https://github.com/kakaobrain/CheXGPT.