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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.11505 |
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| _version_ | 1866910686179753984 |
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| author | Gu, Jawook You, Kihyun Cho, Han-Cheol Kim, Jiho Hong, Eun Kyoung Roh, Byungseok |
| author_facet | Gu, Jawook You, Kihyun Cho, Han-Cheol Kim, Jiho Hong, Eun Kyoung Roh, Byungseok |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_11505 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CheX-GPT: Harnessing Large Language Models for Enhanced Chest X-ray Report Labeling Gu, Jawook You, Kihyun Cho, Han-Cheol Kim, Jiho Hong, Eun Kyoung Roh, Byungseok Computation and Language Information Retrieval 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. |
| title | CheX-GPT: Harnessing Large Language Models for Enhanced Chest X-ray Report Labeling |
| topic | Computation and Language Information Retrieval |
| url | https://arxiv.org/abs/2401.11505 |