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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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医疗应用的快速工程范式:范围审查

Jamil Zaghir1,2, Marco Naguib3, Mina Bjelogrlic1,2

  • 1Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland.

Journal of medical Internet research
|September 10, 2024
PubMed
概括

医疗应用的快速工程正在出现,快速设计是最常见的. 许多研究缺乏基线和详细报告,阻碍了临床自然语言处理的未来研究.

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在法律上,LLMs.在NLP中,我们使用了NLP.临床自然语言处理 临床自然语言处理临床实践中的临床实践计算机科学 计算机科学大型语言模型.医疗应用 医疗应用医疗应用 医疗应用医疗信息学是一门医学信息学专业.医学文本 医学文本医学 医学 医学 医学 医学自然语言处理自然语言处理.隐私 隐私 隐私 隐私 隐私 隐私设计提示 设计提示快速的工程迅速的工程快速学习 快速学习快速调整调整的提示范围审查 范围审查审查

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科学领域:

  • 医疗信息学 医疗信息学
  • 人工智能的人工智能
  • 自然语言处理自然语言处理.

背景情况:

  • 快速工程增强了大型语言模型 (LLM) 功能,由于专门的语言,在医学中尤其重要.
  • 临床自然语言处理 (NLP) 面临复杂的医疗文本和隐私方面的挑战.
  • 快速工程提供了一种用于从医学文本中提取临床信息的新方法.

研究的目的:

  • 审查医疗应用中的快速工程研究和技术方法.
  • 为临床实践提供机会和挑战的概述.
  • 为未来的医疗快速工程研究制定报告准则.

主要方法:

  • 搜索医疗,计算机科学和信息学数据库,包括预印件.
  • 包括使用快速学习 (PL),快速调整 (PT) 和快速设计 (PD) 的2022-2024年的研究.
  • 提取了有关即时范式,LLM,语言,主题,基线和策略的数据.

主要成果:

  • 包括114个快速工程研究;快速设计 (PD) 是最普遍的 (78篇论文).
  • 聊天GPT是最常用的LLM;7项研究使用它与敏感的临床数据.
  • 思想链是最常见的PD技术;许多PD研究缺乏非提示基线.

结论:

  • 快速工程在医学上显示出希望,但报告存在不一致性.
  • 提供了指导方针,以改善未来的研究和报告标准.
  • 总结的数据旨在促进医疗快速工程的进一步进步.