手术知情同意的大型语言模型:对模拟同情心的伦理观点
Pranab Rudra1, Wolf-Tilo Balke2, Tim Kacprowski3,4
1Institute for Ethics, History and Philosophy of Medicine, Hannover Medical School, Hannover, Germany pranab.rudra@stud.mh-hannover.de.
Journal of medical ethics
|March 12, 2025
概括
大型语言模型 (LLM) 通过识别患者的痛苦,而不是模拟同情,可以在道德上改善外科知情同意. 这种方法支持外科医生,并保持了以患者为中心的护理所必不可少的人际联系.
科学领域:
- 医学伦理 医学伦理
- 医疗保健中的人工智能
- 手术患者护理手术患者护理
背景情况:
- 在手术中,知情同意需要准确的信息和同情的参与,以建立信任.
- 大型语言模型 (LLM) 提供了通过信息检索和模拟情绪反应来增强知情同意的机会.
- 关于患者自主权,信任和透明度的伦理问题与LLMs中的模拟同情心产生.
研究的目的:
- 检查手术知情同意的挑战.
- 在这种情况下,评估LLMs的好处和局限性.
- 探索LLM中模拟的同情心对知情同意的伦理影响.
主要方法:
- 区分主动的同理心 (模拟情绪) 和被动的同理心 (识别危险线索).
- 分析LLM在识别和信号患者焦虑方面的潜力.
- 建议一个框架,以道德LLM整合在知情同意.
主要成果:
- 通过LLM进行积极的同情心模拟,有可能误导患者并破坏信任.
- 消极的同情心,专注于识别和发出痛苦的信号,在道德上是可取的.
- 法律医生可以分析患者的线索,以提醒医疗保健提供者对焦虑.
结论:
- 应利用LLM进行被动同情,识别患者的紧急信号.
- 这种方法利用了LLM的分析优势,同时保持了真正的人类同情心.
- 在知情同意中使用伦理法学士学位支持,而不是取代,对于以患者为中心的护理至关重要的关系信任.
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