arXiv:2410.12793cs.CYcs.AI2024-10被引 13

36所医学中心调研揭示生成式AI在医疗中的落地现状与挑战

Environment Scan of Generative AI Infrastructure for Clinical and Translational Science

  • 通过向36家机构负责人问卷调查,摸清生成式AI部署现状
  • 多数机构仍处实验阶段,治理模式集中但培训与伦理缺位
  • 适合医疗管理者、科研人员及政策制定者参考决策

本研究对美国国家转化科学临床研究中心(NCATS)资助的36所临床与转化科学奖(CTSA)项目机构开展了全面的生成式人工智能(GenAI)基础设施环境扫描。随着大语言模型(LLMs)等技术迅猛发展,医疗机构面临前所未有的机遇与挑战。研究通过向各机构领导者(代表学术医疗中心与医疗系统)发放问卷,评估其在生成式AI采纳方面的准备程度与策略,重点关注利益相关方角色、治理结构与伦理考量。结果显示,各机构策略差异显著,多数处于生成式AI部署的实验阶段。治理模式呈现集中化倾向,但在员工培训与伦理监督方面存在明显缺口。研究强调需加强高级管理层、临床医生、信息科技人员与研究人员间的协作,以建立更协调的治理机制。同时发现,生成式AI偏见、数据安全与利益相关方信任等问题亟待解决,以确保技术的伦理化与高效应用。本研究为医疗机构利用生成式AI提升诊疗质量与运营效率提供了重要参考路径。

原文摘要 · Abstract (English)

This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the Clinical and Translational Science Award (CTSA) Program led by the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) at the United States. With the rapid advancement of GenAI technologies, including large language models (LLMs), healthcare institutions face unprecedented opportunities and challenges. This research explores the current status of GenAI integration, focusing on stakeholder roles, governance structures, and ethical considerations by administering a survey among leaders of health institutions (i.e., representing academic medical centers and health systems) to assess the institutional readiness and approach towards GenAI adoption. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The study highlights significant variations in governance models, with a strong preference for centralized decision-making but notable gaps in workforce training and ethical oversight. Moreover, the results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis also reveals concerns regarding GenAI bias, data security, and stakeholder trust, which must be addressed to ensure the ethical and effective implementation of GenAI technologies. This study offers valuable insights into the challenges and opportunities of GenAI integration in healthcare, providing a roadmap for institutions aiming to leverage GenAI for improved quality of care and operational efficiency.

生成式AI医疗信息化治理框架伦理审查

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