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.
European journal of cardiovascular nursing
|January 4, 2024
概括
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.
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.
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