大型语言模型和用户信任:自我引用式学习循环的后果和医疗保健专业人员的技能缺失
Avishek Choudhury1, Zaira Chaudhry1
1Industrial and Management Systems Engineering, West Virginia University, Morgantown, WV, United States.
Journal of medical Internet research
|April 25, 2024
概括
医疗保健中的大型语言模型 (LLM) 需要仔细整合. 临床医生的信任,人工智能生成的数据循环和潜在的失业需要对安全有效的患者护理进行关键的监督.
科学领域:
- 医疗保健信息学 医疗保健信息学
- 人工智能在医学中的应用
- 临床决策支持 临床决策支持
背景情况:
- 医疗保健行业正在迅速采用大型语言模型 (LLM).
- 了解LLM对临床医生的信任和人工智能生成数据的影响至关重要.
- 潜在的陷包括自我参考学习循环和人工智能算法中的偏差放大.
研究的目的:
- 探索临床医生对LLM的信任与人工智能生成的数据之间的关系.
- 分析LLM整合对临床医生的能力和绩效的影响.
- 确定在医疗保健环境中安全有效使用LLM的策略.
主要方法:
- 在医疗保健LLM整合的文献综述和概念分析.
- 检查临床医生信任动态和数据源转换.
- 讨论潜在的风险,包括自我参考的学习循环和非培训.
主要成果:
- 在LLM的自我参考学习循环对数据的多样性和有效性构成风险.
- 专家临床医生可以利用LLM提高生产力,但需要严格的监督.
- 过度依赖LLM可能会导致医疗保健专业人员失去培训.
结论:
- 一种平衡信任和怀疑的战略方法对于LLM整合至关重要.
- 保持临床医生的专业知识和对AI输出的关键参与是必不可少的.
- 解决法律和道德方面的考虑对于负责任的LLM在医疗保健中的部署是必要的.
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