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科学领域:

  • 在瘤学中使用人工智能
  • 医疗信息学 医疗信息学
  • 生物伦理学生物伦理学

背景情况:

  • 大型语言模型 (LLM) 在瘤学任务中表现有前途,例如癌症分期.
  • 包括数据隐私,偏见和透明度在内的伦理问题阻碍了在高风险的医疗环境中采用LLM.

研究的目的:

  • 探索瘤学LLM应用的伦理挑战.
  • 评估解决这些伦理问题的新兴技术.

主要方法:

  • 按照PRISMA指南进行系统审查.
  • 搜索了八个学术数据库 (2019年1月至2024年12月).
  • 包括来自4,319篇获取文章的65篇相关出版物.

主要成果:

  • 确定了六个普遍的伦理挑战:信任,公平,隐私,透明度,非有害性和问责制.
  • 评估了技术解决方案,以减轻这些道德问题.
  • 总结了评估解决方案有效性的指标.

结论:

  • 为负责的LLM在瘤学部署提供了可行的建议.
  • 旨在确保道德AI的遵守,并改善患者的治疗结果.
  • 建立了瘤学道德AI的框架,并确定了未来的研究方向.