临床实验室检测监管为医学人工智能的本地监督提供的经验教训。
Lessons for local oversight of AI in medicine from the regulation of clinical laboratory testing.
作者信息
Herman Daniel S, Reece Jenna T, Weissman Gary E
机构信息
Department of Pathology & Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, PA, USA.
出版信息
NPJ Digit Med. 2024 Dec 13;7(1):359. doi: 10.1038/s41746-024-01369-1.
Abstract
Current regulatory frameworks for artificial intelligence-based clinical decision support (AICDS) are insufficient to ensure safety, effectiveness, and equity at the bedside. The oversight of clinical laboratory testing, which requires federal- and hospital-level involvement, offers many instructive lessons for how to balance safety and innovation and warnings regarding the fragility of this balance. We propose an AICDS oversight framework, modeled after clinical laboratory regulation, that is deliberative, inclusive, and collaborative.
摘要
当前基于人工智能的临床决策支持（AICDS）监管框架不足以确保床边的安全性、有效性和公平性。临床实验室检测的监管需要联邦和医院层面的参与，为如何平衡安全性与创新性提供了许多有益的经验教训，也警示了这种平衡的脆弱性。我们提出了一个仿照临床实验室监管模式的AICDS监管框架，该框架具有审议性、包容性和协作性。
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