大型语言模型可以帮助生物统计学和放射学研究所需的编码
Adarsh Ghosh1, Hailong Li2, Andrew T Trout3
1Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio (A.G., H.L., A.T.T.); Department of Radiology, Nationwide Children's Hospital, Columbus, Ohio (A.G.).
Academic radiology
|October 15, 2024
概括
大型语言模型 (LLM) 可以通过生成用于统计分析和深度学习任务的代码来帮助放射学研究人员. 虽然对于初始草稿很有用,但由于潜在的错误和幻觉,研究人员必须验证LLM生成的代码.
科学领域:
- 放射学研究的研究辐射学.
- 医学成像分析分析 医学成像分析
- 医学中的人工智能
背景情况:
- 放射学研究经常涉及复杂的数据处理,统计分析和编码.
- 大型语言模型 (LLM) 已经在生物信息学中证明了其能力,这表明其在放射学中的潜在应用.
研究的目的:
- 评估LLMs在为放射学研究提供统计和深度学习解决方案和代码方面的有效性.
- 为了比较ChatGPT-4o,ChatGPT-3.5和Google Gemini在生成生物统计和深度学习任务代码方面的性能.
主要方法:
- 通过使用基于Web的聊天接口来评估LLM (ChatGPT-4o,ChatGPT-3.5,Gemini).
- 实验1涉及生成用于统计测试和数据可视化的R代码,使用癌症成像档案数据集.
- 实验2涉及生成用于使用RSNA-STR数据集检测肺炎的基于变压器的深度学习模型的Python代码.
主要成果:
- 与ChatGPT-3.5和Gemini相比,ChatGPT-4o在8种场景中的7种场景中提供了正确的统计答案,生成的R代码中的运行时错误较少.
- 聊天GPT-4o和Gemini都成功生成了用于深度学习任务的初始Python代码,并通过代提示解决了错误.
- 在提供基线代码和协助放射学研究任务的错误解决方面,LLM 已经证明了其实用性.
结论:
- 法律学可以有效地协助放射学研究的编码,为数据可视化,统计测试和深度学习模型提供初始代码.
- 由于潜在的不准确性,幻觉和数据隐私问题,研究人员必须批判性地审查和验证LLM生成的代码.
- 对于那些在放射学研究中需要基础代码和故障排除帮助的研究人员来说,LLM是有价值的工具.
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