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医疗保健领域应用人工智能的框架。

A Framework for Applied AI in Healthcare.

作者信息

Truong Tran, Gilbank Paige, Johnson-Cover Kaleigh, Ieraci Adriana

机构信息

Techna Institute, University Health Network, Toronto, Ontario, Canada.

Translational Research Program, University of Toronto, Toronto, Ontario, Canada.

出版信息

Stud Health Technol Inform. 2019 Aug 21;264:1993-1994. doi: 10.3233/SHTI190751.

DOI:10.3233/SHTI190751
PMID:31438445
Abstract

Significant efforts are being made to develop artificial intelligence technologies for health settings. In a health system that has been notoriously slow to adopt innovative technologies, it is important to consider the implementation of a new technology early in the development stage, especially one that will have added challenges of trust and transparency. To facilitate this process, an implementation framework for artificial intelligence technologies in clinical settings has been created.

摘要

目前正在大力开发用于医疗环境的人工智能技术。在一个采用创新技术一直非常缓慢的医疗系统中,在开发阶段尽早考虑新技术的实施非常重要,尤其是对于一项将带来信任和透明度等额外挑战的技术。为推动这一进程,已创建了一个临床环境中人工智能技术的实施框架。

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2
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JMIRx Med. 2025 Feb 20;6:e65565. doi: 10.2196/65565.
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Implementation frameworks for end-to-end clinical AI: derivation of the SALIENT framework.端到端临床人工智能实施框架:SALIENT 框架的推导。
J Am Med Inform Assoc. 2023 Aug 18;30(9):1503-1515. doi: 10.1093/jamia/ocad088.
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Implementation Frameworks for Artificial Intelligence Translation Into Health Care Practice: Scoping Review.
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J Med Internet Res. 2022 Jan 27;24(1):e32215. doi: 10.2196/32215.
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Metabolomic Laboratory-Developed Tests: Current Status and Perspectives.代谢组学实验室自主研发的检测方法:现状与展望。
Metabolites. 2021 Jun 26;11(7):423. doi: 10.3390/metabo11070423.
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The Greatest Challenge to Using AI/ML for Primary Health Care: Mindset or Datasets?将人工智能/机器学习应用于初级卫生保健面临的最大挑战:思维模式还是数据集?
Front Artif Intell. 2020 Aug 21;3:53. doi: 10.3389/frai.2020.00053. eCollection 2020.