医疗分类作为人工智能伦理基准
Nathalie Maria Kirch1,2, Konstantin Hebenstreit1, Matthias Samwald3
1Institute of Artificial Intelligence, Medical University of Vienna, 1090, Vienna, Austria.
Scientific reports
|August 22, 2025
概括
TRIAGE基准显示,大型语言模型 (LLM) 与医学伦理有很大的问题. 开源人工智能比专有人工智能犯了更多的错误,而道德指导在大规模伤亡场景中恶化了性能.
科学领域:
- 医学伦理
- 人工智能
- 机器学习
背景情况:
- 评估人工智能的道德决策至关重要,尤其是在高风险的医疗场景中.
- 现有的基准可能无法完全理解现实世界的道德困境.
- 人工智能系统面临着独特的伦理挑战.
研究的目的:
- 引入TRIAGE基准来评估AI在大规模伤亡事件中的道德决策.
- 评估主要大型语言模型 (LLM) 在医学伦理困境中的表现.
- 调查伦理和对抗性提示对LLM行为的影响.
主要方法:
- 使用医疗专业人员的医疗困境开发TRIAGE基准.
- 使用TRIAGE基准的六个著名的LLM的评估.
- 基于不同提示策略 (道德原则,对抗提示) 的性能变化分析.
主要成果:
- 大多数LLM超出了随机猜测,但显示出显著的伦理错误,特别是开源模型.
- 为LLM提供明确的道德原则降低了他们在TRIAGE上的表现.
- 对立的提示大大降低了LLM道德决策的准确性.
结论:
- 医学伦理学士学位的表现非常敏感,
- 目前的人工智能系统在医学中的高风险伦理决策方面存在局限性.
- 需要进一步的研究来改善AI在关键医疗状况中的道德推理.
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