Department of Neurology, University of Washington (UW) School of Medicine, USA; UW Medicine Sleep Center, USA.
EnsoData, USA.
Sleep Med Rev. 2021 Oct;59:101512. doi: 10.1016/j.smrv.2021.101512. Epub 2021 Jun 2.
Artificial intelligence (AI) allows analysis of "big data" combining clinical, environmental and laboratory based objective measures to allow a deeper understanding of sleep and sleep disorders. This development has the potential to transform sleep medicine in coming years to the betterment of patient care and our collective understanding of human sleep. This review addresses the current state of the field starting with a broad definition of the various components and analytic methods deployed in AI. We review examples of AI use in screening, endotyping, diagnosing, and treating sleep disorders and place this in the context of precision/personalized sleep medicine. We explore the opportunities for AI to both facilitate and extend providers' clinical impact and present ethical considerations regarding AI derived prognostic information. We cover early adopting specialties of AI in the clinical realm, such as radiology and pathology, to provide a road map for the challenges sleep medicine is likely to face when deploying this technology. Finally, we discuss pitfalls to ensure clinical AI implementation proceeds in the safest and most effective manner possible.
人工智能（AI）允许分析“大数据”，将临床、环境和实验室的客观指标相结合，以更深入地了解睡眠和睡眠障碍。这一发展有可能在未来几年彻底改变睡眠医学，从而改善患者护理和我们对人类睡眠的整体认识。本综述首先从 AI 中使用的各种组件和分析方法的广义定义开始，介绍该领域的现状。我们回顾了 AI 在睡眠障碍的筛查、分型、诊断和治疗中的应用实例，并将其置于精准/个性化睡眠医学的背景下。我们探讨了 AI 为医疗服务提供者的临床工作带来便利和扩展的机会，并介绍了与 AI 衍生预后信息相关的伦理问题。我们介绍了 AI 在临床领域的早期应用专业，如放射学和病理学，为睡眠医学在部署这项技术时可能面临的挑战提供了路线图。最后，我们讨论了一些需要注意的问题，以确保临床 AI 的实施以最安全和最有效的方式进行。
