Goldstein Cathy A, Berry Richard B, Kent David T, Kristo David A, Seixas Azizi A, Redline Susan, Westover M Brandon, Abbasi-Feinberg Fariha, Aurora R Nisha, Carden Kelly A, Kirsch Douglas B, Malhotra Raman K, Martin Jennifer L, Olson Eric J, Ramar Kannan, Rosen Carol L, Rowley James A, Shelgikar Anita V
Sleep Disorders Center, Department of Neurology, University of Michigan, Ann Arbor, Michigan.
Division of Pulmonary, Critical Care and Sleep Medicine, University of Florida, Gainesville, Florida.
J Clin Sleep Med. 2020 Apr 15;16(4):605-607. doi: 10.5664/jcsm.8288.
Sleep medicine is well positioned to benefit from advances that use big data to create artificially intelligent computer programs. One obvious initial application in the sleep disorders center is the assisted (or enhanced) scoring of sleep and associated events during polysomnography (PSG). This position statement outlines the potential opportunities and limitations of integrating artificial intelligence (AI) into the practice of sleep medicine. Additionally, although the most apparent and immediate application of AI in our field is the assisted scoring of PSG, we propose potential clinical use cases that transcend the sleep laboratory and are expected to deepen our understanding of sleep disorders, improve patient-centered sleep care, augment day-to-day clinical operations, and increase our knowledge of the role of sleep in health at a population level.
睡眠医学有望受益于利用大数据创建人工智能计算机程序的进展。睡眠障碍中心一个明显的初始应用是在多导睡眠图(PSG)期间对睡眠及相关事件进行辅助(或增强)评分。本立场声明概述了将人工智能(AI)整合到睡眠医学实践中的潜在机遇和局限性。此外,尽管AI在我们领域最明显和直接的应用是PSG的辅助评分,但我们提出了一些潜在的临床用例,这些用例超越了睡眠实验室,有望加深我们对睡眠障碍的理解,改善以患者为中心的睡眠护理,增强日常临床操作,并在人群层面增加我们对睡眠在健康中的作用的认识。