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We systematically investigate lightweight strategies to adapt large language models (LLMs) for the task of radiology report summarization (RRS).
Language models are few-shot learners
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Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
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Tianyi Zhang*, Varsha Kishore*, Felix Wu*, Kilian Q. Weinberger, and Yoav Artzi. 2020 · 2020
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Optimizing the factual correctness of a summary: A study of summarizing radiology reports
Yuhao Zhang, Derek Merck, Emily Bao Tsai, Christopher D. Manning, and Curtis P. Langlotz. 2020 · 2020
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Overview of the mediqa 2021 shared task on summarization in the medical domain
Asma Ben Abacha, Yassine M’rabet, Yuhao Zhang, Chaitanya Shivade, Curtis Langlotz, and Dina Demner-Fushman. 2021 · 2021
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Bdkg at mediqa 2021: system report for the radiology report summarization task
Songtai Dai, Quan Wang, Yajuan Lyu, and Yong Zhu. 2021 · 2021
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Mengjie Zhao and Hinrich Schütze. 2021 · 2021
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Evidence extraction to validate medical claims in fake news detection
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Improving the factual correctness of radiology report generation with semantic rewards
Jean-Benoit Delbrouck, Pierre Chambon, Christian Bluethgen, Emily Tsai, Omar Almusa, and Curtis Langlotz. 2022a · 2022
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Edward Hu, Yelong Shen, Phil Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Lu Wang, and Weizhu Chen. 2021 · 2021
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Mimic-iv
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Long N Phan, James T Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, and Grégoire Altan-Bonnet. 2021 · 2021
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Large language models encode clinical knowledge
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Overview of the radsum23 shared task on multi-modal and multi-anatomical radiology report summarization
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