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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.).

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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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科学领域:

  • 医疗成像中的人工智能
  • 放射学自然语言处理用于放射学.
  • 医疗保健中的机器学习

背景情况:

  • 大型语言模型 (LLM) 正在迅速发展,有许多商业和开源选项可供选择.
  • 之前的研究集中在GPT-4用于放射学报告分析,但与领先的开源LLM缺乏现实世界的比较.
  • 从胸部X射线图报告中准确地提取发现对于临床决策至关重要.

研究的目的:

  • 将领先的开源LLM与GPT-4的性能进行比较,从胸部X射线图报告中提取相关发现.
  • 在这个任务中评估零射击和少数射击提示策略的有效性.

主要方法:

  • 对两个独立数据集的自由文本胸部X光学报告 (ImaGenome和马萨诸塞州总医院) 的回顾性分析.
  • 商业型号 (GPT-3.5 Turbo,GPT-4) 与开源型号 (Mistral-7B,Mixtral-8×7B,Llama 2-13B,Llama 2-70B,Qwen1.5-72B) 和CheXbert/CheXpert-labeler.com) 的比较,这些商业型号的使用情况是如下:
  • 使用零射击和少数射击提示进行评估,通过F1得分测量性能,并使用McNemar测试进行比较.

主要成果:

  • 在ImaGenome数据集上,Llama 2-70B获得了0.97 (零射击) 和0.97 (少数射击) 的微F1得分,与GPT-4的0.98.8密切匹配.
  • 在机构数据集上,一个集成开源模型实现了微F1得分0.96 (零射击) 和0.97 (少数射击),与GPT-4的0.98和0.97.97.相当.
  • GPT-4在零射击标签方面表现出优越性,但少数射击提示显著改善了开源模型的性能,产生了可比的结果.

结论:

  • 虽然GPT-4在零射击报告标签方面表现出色,但使用最小的示例进行少数射击提示使开源LLM能够达到接近GPT-4的性能水平.
  • 在不同的数据集和LLM架构中,几次射击提示的有效性各不相同.
  • 开源的LLM显示了在放射学报告分析中的临床应用的巨大潜力,特别是当它们与少数射击学习进行微调时.