.png){kind=icon}
返回
From Code to Bedside: Implementing Artificial Intelligence Using Quality Improvement Methods
.png){kind=icon}
OA
AI
DOI：10.1007/s11606-020-06394-w.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Despite increasing interest in how artificial intelligence (AI) can augment and improve healthcare delivery, the development of new AI models continues to outpace adoption in existing healthcare processes. Integration is difficult because current approaches separate the development of AI models from the complex healthcare environments in which they are intended to function, resulting in models developed without a clear and compelling use case and not tested or scalable in a clinical setting. We propose that current approaches and traditional research methods do not support successful AI implementation in healthcare and outline a repeatable mixed-methods approach, along with several examples, that facilitates uptake of AI technologies into human-driven healthcare processes. Unlike traditional research, these methods do not seek to control for variation, but rather understand it to learn how a technology will function in practice coupled with user-centered design techniques. This approach, leveraging design thinking and quality improvement methods, aims to increase the adoption of AI in healthcare and prompt further study to understand which methods are most successful for AI implementations.
Keyword:
artificial intelligence
quality improvement
design thinking
implementation science
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.2
论文数:
1.3W
被引数:
3.2W
机构
引用论文
Pseudo-Kaposi’s sarcoma of the hand associated with an acquired iatrogenic arteriovenous fistula.png)
IF0
PREAI
分享收藏Key challenges for delivering clinical impact with artificial intelligence通过人工智能实现临床影响的关键挑战BMC MEDICINEOAAI
BMC MEDICINEIF8.3
OAAI分享收藏Segmented organosiloxane copolymers: 2 Thermal and mechanical properties of siloxane—urea copolymersPolymer
PolymerIF0
PREAI
分享收藏
查看更多内容