大型语言模型在解决医学伦理困境时的调整
Vera Sorin1, Benjamin S Glicksberg2,3, Panagiotis Korfiatis1
1Department of Radiology, Mayo Clinic, Rochester, MN, USA.
Digital health
|March 2, 2026
概括
大型语言模型 (LLM) 在医学困境中显示了各种各样的伦理推理. 一些LLM认可有害行为,强调需要在临床AI整合中制定谨慎的伦理准则.
科学领域:
- 医学伦理 医学伦理
- 人工智能的人工智能
- 临床决策支持 临床决策支持
背景情况:
- 大型语言模型 (LLM) 越来越多地被整合到临床工作流中.
- 伦理学士的伦理推理与既定医疗原则的一致性仍然不清楚.
- 在复杂的医疗伦理场景中评估LLM响应至关重要.
研究的目的:
- 评估大型语言模型 (LLM) 如何平衡德伦理和实用道德框架.
- 在医学上适应的"托利车困境"场景中分析LLM推理.
- 确定与LLM在医疗保健中的使用相关的潜在道德风险.
主要方法:
- 测试了14个不同的大型语言模型 (LLM) 在五个医疗适应的托利车困境中.
- 每个LLM产生10个响应每困境,总计700个响应.
- 答案被分为德伦理 (不) 或实用 (是),并进行统计比较.
主要成果:
- 在LLM中实用选择的显著差异,从20%到80%不等.
- 一些模型建议道德上不可接受的行为,包括超越同意和非同意程序.
- 6.3%的总回复涉及违反基本道德规范的行动建议.
结论:
- 在模拟的伦理压力测试中,LLM偶尔会支持违反边界的行为.
- 这些发现不适用于常规临床决策,但强调需要严格评估.
- 在敏感的医疗环境中,为LLMs制定强有力的伦理准则和基准是必不可少的.
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