CheX-GPT: Harnessing Large Language Models for Enhanced Chest X-ray Report Labeling

• URL: http://arxiv.org/abs/2401.11505v2
• Date: Wed, 06 Nov 2024 04:11:14 GMT
• Title: CheX-GPT: Harnessing Large Language Models for Enhanced Chest X-ray Report Labeling
• Authors: Jawook Gu, Kihyun You, Han-Cheol Cho, Jiho Kim, Eun Kyoung Hong, Byungseok Roh,
• Abstract summary: Free-text radiology reports present a rich data source for various medical tasks, but effectively labeling these texts remains challenging. Our study offers three main contributions: 1) We demonstrate the potential of GPT as an adept labeler using carefully designed prompts; 2) We trained a BERT-based labeler, CheX-GPT, which operates faster and more efficiently than its GPT counterpart; and 3) To benchmark labeler performance, we introduced a publicly available expert-annotated test set, MIMIC-500.
• Score: 6.813646734420541
• License:
• Abstract: 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/Soombit-ai/CheXGPT.

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