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Updated: Jun 17, 2026

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商业和开源大型语言模型的比较,用于标记胸部X射线图报告

Felix J Dorfner1, Liv Jürgensen1, Leonhard Donle1

  • 1From the Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, 149 Thirteenth St, Charlestown, MA 02129 (F.J.D., T.R.B., M.C.C., A.E.K., C.P.B.); Department of Radiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany (F.J.D., L.D., F.A.M., F.B., L.J.); Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, Mass (L.J.); Department of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany (L.C.A.); Mass General Brigham Data Science Office, Boston, Mass (J.S., T.S., C.P.B.); Microsoft Health and Life Sciences (HLS), Redmond, Wash (J.M.); Klinikum rechts der Isar, Technical University of Munich, Munich, Germany (K.K.B.); Department of Radiology and Nuclear Medicine, German Heart Center Munich, Munich, Germany (K.K.B.); and Department of Cardiovascular Radiology and Nuclear Medicine, Technical University of Munich, School of Medicine and Health, German Heart Center, TUM University Hospital, Munich, Germany (K.K.B.).

Radiology
|October 29, 2024
PubMed
概括

在零射击胸部X射线报告标签方面,GPT-4略高于开源大型语言模型 (LLM). 然而,用例子提示少数人,缩小了绩效差距,显示了开源LLMs的可比结果.

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