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如何解释人工智能可以增加或减少临床医生对医疗保健中的AI应用的信任:系统性审查

Rikard Rosenbacke1, Åsa Melhus2, Martin McKee3

  • 1Centre for Corporate Governance, Department of Accounting, Copenhagen Business School, Frederiksberg, Denmark.

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概括
此摘要是机器生成的。

可解释的人工智能 (XAI) 可以增强临床医生对人工智能 (AI) 决策的信心,但只有当解释清晰和相关时. 设计不佳的解释可能会降低信任,强调需要在医疗保健中平衡地整合人工智能.

关键词:
在XAI,XAI就是XAI.基于影响的措施.在临床决策过程中.临床信息学 临床信息学临床使用 临床使用临床医生信任的信任认知措施是指认知措施.可解释的人工智能值得信赖的AI 值得信赖的AI

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科学领域:

  • 医疗信息学 医疗信息学
  • 医疗保健中的人工智能
  • 人与计算机的交互

背景情况:

  • 人工智能 (AI) 在临床实践中提供了巨大的潜力,但它的
  • 黑盒子是一个黑盒子.
  • 自然性可能会阻碍临床医生的采用.
  • 可解释人工智能 (XAI) 旨在通过提供对人工智能决策过程的见解来建立信任.
  • 确保适当的临床医生信任对于有效的AI集成至关重要,避免过度依赖和怀疑.

研究的目的:

  • 系统地审查和合成关于可解释AI (XAI) 如何影响临床医生对人工智能驱动的临床决策的信心的经验证据.
  • 通过AI解释在医疗保健环境中了解信任调制的细微差别.
  • 确定影响XAI在培养适当水平的临床医生信任方面的有效性因素.

主要方法:

  • 按照PRISMA指南进行的系统审查,搜索PubMed和科学网.
  • 纳入标准侧重于实证研究,衡量XAI对临床医生的信任的影响 (认知或基于影响的措施).
  • 在778篇选文章中,包括了10项研究,对每项研究进行了偏差风险评估.

主要成果:

  • 大多数研究 (5/10) 发现,与标准人工智能相比,XAI增加了临床医生的信任,特别是具有清晰,简洁和临床相关的解释.
  • 三项研究报告XAI对信任没有显著影响,表明解释不会自动改善信任.
  • 两项研究表明,基于解释的复杂性和连贯性,XAI可以增强或减少信任,强调解释质量的关键作用.

结论:

  • 解释的质量和清晰度显著调节临床医生对AI的信任,复杂或矛盾的解释可能会破坏它.
  • 实现适当的信任平衡是必不可少的,防止盲目信任和对AI建议过度怀疑.
  • 需要进一步的研究来完善信任措施,并制定最佳AI整合到临床实践中的战略.