Department of Computer Science, Stanford University, Stanford, CA, USA.
Clinical Excellence Research Center, Stanford University School of Medicine, Stanford, CA, USA.
Nature. 2020 Sep;585(7824):193-202. doi: 10.1038/s41586-020-2669-y. Epub 2020 Sep 9.
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.
机器学习和非接触式传感器的进步催生了环境智能——对人类的存在敏感且做出响应的物理空间。在这里,我们回顾一下这项技术如何帮助我们更好地理解医疗保健领域中隐喻意义上的黑暗、不可观测的空间。在医院空间中,早期的应用很快就能使重症监护病房和手术室的临床工作流程更加高效,并提高患者安全性。在日常生活空间中,环境智能可以通过了解日常行为,延长老年人的独立性,并改善慢性病患者的管理。与其他技术一样,要想将这项技术大规模转化为临床应用,必须克服严格的临床验证、适当的数据隐私和模型透明度等挑战。明智地使用这项技术将使我们能够理解物理环境与对健康至关重要的人类行为之间的复杂相互作用。