医疗技术评估中的生成人工智能:机遇,挑战和政策考虑:ISPOR工作组的报告
Rachael L Fleurence1, Jiang Bian2, Xiaoyan Wang3
1Office of the Director, National Institutes of Health, National Institute of Biomedical Imaging and Bioengineering, Bethesda, MD, USA.
概括
生成型人工智能 (AI) 对医疗技术评估 (HTA) 是有前途的,有助于文献审查,现实世界的证据分析和经济建模,但由于能力和局限性的不断发展,需要仔细评估.
科学领域:
- 卫生技术评估 卫生技术评估
- 人工智能的人工智能
- 基金会模型 基金会模型
背景情况:
- 包括大型语言模型 (LLM) 在内的生成人工智能 (AI) 正在迅速发展.
- 这些技术在各种科学和医疗保健领域提供了潜在的应用.
研究的目的:
- 在卫生技术评估 (HTA) 中引入生成性AI和基础模型的应用.
- 探索将这些先进的人工智能工具集成到HTA工作流程中的潜在好处和局限性.
主要方法:
- 在HTA的三个关键领域对生成AI应用进行了审查.
- 专注于系统的文献评论,现实世界的证据分析和健康经济建模.
主要成果:
- 生成型人工智能可以自动化系统文献审查中的任务,例如数据提取和元分析代码生成.
- 它可以促进对大型现实世界数据集的分析,包括非结构化的临床笔记和成像数据.
- 人工智能协助健康经济模型的开发,从概念化到验证.
- 确定的局限性包括对科学严谨性,可靠性,偏见,公平性,数据隐私和监管遵守的担忧.
结论:
- 生成型人工智能为增强医疗技术评估 (HTA) 过程提供了巨大的潜力.
- 持续的快速发展需要对HTA中的AI应用进行持续,仔细的评估.
- 用户和开发人员必须了解这些不断发展的人工智能工具的当前能力和局限性,强调人类监督.
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