Illuminating the dark spaces of healthcare with ambient intelligence

被引:152
|
作者
Haque, Albert [1 ]
Milstein, Arnold [2 ]
Li Fei-Fei [1 ,3 ]
机构
[1] Stanford Univ, Dept Comp Sci, Stanford, CA 94305 USA
[2] Stanford Univ, Sch Med, Clin Excellence Res Ctr, Stanford, CA 94305 USA
[3] Stanford Univ, Stanford Inst Human Ctr Artificial Intelligence, Stanford, CA 94305 USA
关键词
UNITED-STATES; GAIT ANALYSIS; ARTIFICIAL-INTELLIGENCE; SPEECH RECOGNITION; COMPUTER VISION; SURGICAL SKILL; FALL DETECTION; SENSOR SYSTEM; MENTAL-HEALTH; HAND HYGIENE;
D O I
10.1038/s41586-020-2669-y
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Advances in machine learning and contactless sensors have given rise to ambient intelligence-physical spaces that are sensitive and responsive to the presence of humans. Here we review how this technology could improve our understanding of the metaphorically dark, unobserved spaces of healthcare. In hospital spaces, early applications could soon enable more efficient clinical workflows and improved patient safety in intensive care units and operating rooms. In daily living spaces, ambient intelligence could prolong the independence of older individuals and improve the management of individuals with a chronic disease by understanding everyday behaviour. Similar to other technologies, transformation into clinical applications at scale must overcome challenges such as rigorous clinical validation, appropriate data privacy and model transparency. Thoughtful use of this technology would enable us to understand the complex interplay between the physical environment and health-critical human behaviours.
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页码:193 / 202
页数:10
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