复杂卫生服务中的生成AI决策属性:快速审查
Nandini Doreswamy1,2, Louise Horstmanshof1
1Faculty of Health Sciences, Southern Cross University, Lismore, AUS.
Cureus
|March 3, 2025
概括
生成型人工智能 (GAI) 在医疗保健方面表现有前途,但由于其潜在的偏见和不准确性,它存在风险. 在复杂的卫生服务中广泛采用之前,需要进一步的研究.
科学领域:
- 人工智能的人工智能
- 医疗保健服务研究 医疗服务研究
- 生物伦理学生物伦理学
背景情况:
- 生成型人工智能 (Generative AI或GAI) 在人工智能方面取得了重大进展,它包含了规范性编程规则和受程序员主观性和数据差异影响的描述元素.
- GAI能够产生准确和不准确的信息,支持道德和不道德的决策,以及其固有的缺乏透明度和问责制,这对像政策和监管这样复杂的医疗服务的决策构成风险.
研究的目的:
- 进行快速审查,识别和映射影响复杂卫生服务中生成性AI决策的属性.
- 通过从理性和描述性角度检查其决策过程,确保对生成人工智能设计,工程和使用的伦理方法.
主要方法:
- 采用了遵循PRISMA 2020标准的快速审查方法.
- 为了在ProQuest,Scopus,Web of Science和Google Scholar中进行系统的数据库搜索,建立了包含和排除标准.
- 分析了2023年和2024年初发表的文章,从最初的1,550篇确定文章中选择了21篇.
主要成果:
- 学习,理解和偏见是研究文献中最常引用的生成人工智能的属性.
- 虽然生成人工智能提供了先进的自动化功能,但它存在很大的风险,包括潜在的偏见和"幻觉" (产生虚假信息).
- 确定的主要不利因素包括缺乏道德指南针,同情心和对隐私的考虑.
结论:
- 生成型人工智能的当前局限性,如偏见和缺乏问责制,对复杂的卫生服务中的应用构成重大风险.
- 尽管它具有自动化和模式识别的潜力,但在医疗保健中广泛采用之前,需要进一步开发以解决伦理问题并确保可靠性.
- 该研究强调,需要对生成人工智能的伦理框架和决策过程做更多工作,以减轻卫生政策和监管等敏感领域的风险.
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