作为第三个代理的生成人工智能:大型语言模型和临床医生与患者关系的转变
Hugo de O Campos1, Daniel Wolfe2, Hongzhou Luan2
1Strategic Advisory Board, Computational Precision Health, University of California, Berkeley and University of California, San Francisco, 2195 Hearst Ave, Suite 120, Oakland, CA, 94720, United States.
Journal of participatory medicine
|August 19, 2025
概括
人工智能 (AI) 和大型语言模型 (LLM) 提供了改善患者护理和参与的新方法. 然而,仔细考虑道德挑战和人类因素对于成功融入患者与临床医生的关系至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 人与计算机的交互
背景情况:
- 人工智能 (AI),特别是大型语言模型 (LLM) 的整合到医疗保健中,为患者与临床医生的互动带来了新的机遇和挑战.
- 围绕LLM采用现有的伦理和实际考虑需要仔细检查,特别是关于它们对患者体验的影响.
研究的目的:
- 分析LLM作为患者与临床医生关系中的促进者或中断者的潜在作用,重点是增强患者体验.
- 探索LLM如何改善患者参与,帮助分拣过程,并支持临床决策.
主要方法:
- 一个定性检查,借鉴患者倡导者,计算机科学家和医生信息学家的跨学科经验.
- 分析与医疗保健环境中LLM相关的潜在好处和风险,包括数据隐私,算法偏见和人类联系的保护.
主要成果:
- 临床医疗法可以作为工具来增强患者的参与度,并为临床决策提供信息,这可能导致"AI患者"的出现.
- 有关数据隐私,算法偏见,道德伤害以及在护理中的人类因素的潜在侵蚀存在重大担忧.
- 提出了一个概念框架,以导航LLM整合到患者-临床医生动态的复杂性.
结论:
- 在医疗保健中实现LLM的全部潜力需要一个有意识的方法来识别和保存患者和临床医生关系的独特的人性方面.
- 未来的研究应该将伦理和哲学观点与以患者为中心的设计和透明度等AI原则相结合,促进技术人员,提供者和患者之间的合作.
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