Scholarlink

<p>This scoping review synthesises current evidence on artificial intelligence (AI) governance in healthcare organisations, outlining key components of AI governance frameworks. Following PRISMA-ScR guidelines, we searched MEDLINE, Embase, and Scopus (April 2024, updated March 2025) for AI governance frameworks in acute care. Seventy-seven frameworks were identified and examined for four components: (1) Guiding principles (ethics or governance-related); (2) Assessment methods; (3) AI lifecycle stages; and (4) Oversight mechanisms. Most frameworks were not applicable to real-world healthcare settings and missed key principles or components, such as an oversight mechanism. Only 10 frameworks (13.0%) included all four framework components, with oversight mechanisms (e.g. AI-specific governance committee) being the least common (<i>n</i> = 15, 19.5%). There is a need to move beyond principles to implementing AI governance frameworks in healthcare organisations and evaluating their real-world impact.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Governance for safe and responsible AI in healthcare organisations: a scoping review of frameworks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月1日

查看全文
  • Amy Wang,
  • Sam Freeman,
  • Farah Magrabi

摘要

This scoping review synthesises current evidence on artificial intelligence (AI) governance in healthcare organisations, outlining key components of AI governance frameworks. Following PRISMA-ScR guidelines, we searched MEDLINE, Embase, and Scopus (April 2024, updated March 2025) for AI governance frameworks in acute care. Seventy-seven frameworks were identified and examined for four components: (1) Guiding principles (ethics or governance-related); (2) Assessment methods; (3) AI lifecycle stages; and (4) Oversight mechanisms. Most frameworks were not applicable to real-world healthcare settings and missed key principles or components, such as an oversight mechanism. Only 10 frameworks (13.0%) included all four framework components, with oversight mechanisms (e.g. AI-specific governance committee) being the least common (n = 15, 19.5%). There is a need to move beyond principles to implementing AI governance frameworks in healthcare organisations and evaluating their real-world impact.