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Updated: Jul 6, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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用大型语言模型改变护理:从概念到实践

Brigitte Woo1, Tom Huynh2, Arthur Tang2

  • 1Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.

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大型语言模型 (LLM) 在护理中为患者教育和行政任务提供了巨大的潜力. 然而,像人工智能幻觉和数据隐私等挑战需要谨慎管理,以便安全地整合到医疗保健中.

关键词:
生成型的人工智能 (GAI) 是一种人工智能.大型语言模型.护理 护理 护理技术 技术 技术 技术 技术

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科学领域:

  • 护理信息学 护理信息学
  • 医疗保健中的人工智能

背景情况:

  • 大型语言模型 (LLM) 显示出改变护理实践的希望,包括患者教育,诊断支持和行政效率.
  • 在医疗保健LLMs的整合提出了诸如AI幻觉和数据隐私问题等挑战.

研究的目的:

  • 探索LLMs在护理方面的潜在应用.
  • 识别和解决与在临床环境中实施LLMs相关的挑战.
  • 提出改进LLM准确性和确保数据安全的策略.

主要方法:

  • 关于护理学LLM应用的当前文献的审查.
  • 对减轻LLM限制的拟议方法的分析,包括快速工程,温度调整,模型微调和本地部署.
  • 讨论在患者护理中LLMs的伦理考虑和局限性.

主要成果:

  • 法律学士可以提高患者教育,协助诊断,提供治疗建议,并提高护理的行政任务效率.
  • 诸如快速工程,温度调整,模型微调和本地部署等策略可以帮助提高LLM准确性和数据安全性.
  • 尽管取得了进展,但LLM不能取代人类医疗保健专业人员的关键专业知识和判断力.

结论:

  • 法律学士在护理方面具有变革性的潜力,在各种临床和行政领域提供好处.
  • 解决人工智能幻觉和数据隐私问题对于在医疗保健中负责任地采用LLM至关重要.
  • 一种协同方法,将LLM能力与人类专业知识相结合,对于最佳的患者护理至关重要.