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Review
. 2023 Mar;27(1):39-55.
doi: 10.1007/s11325-022-02592-4. Epub 2022 Mar 9.

Clinical applications of artificial intelligence in sleep medicine: a sleep clinician's perspective

Anuja Bandyopadhyay  1 , Cathy Goldstein  2
Affiliations
Review

Clinical applications of artificial intelligence in sleep medicine: a sleep clinician's perspective

Anuja Bandyopadhyay et al. Sleep Breath. 2023 Mar.

Abstract

Background: The past few years have seen a rapid emergence of artificial intelligence (AI)-enabled technology in the field of sleep medicine. AI refers to the capability of computer systems to perform tasks conventionally considered to require human intelligence, such as speech recognition, decision-making, and visual recognition of patterns and objects. The practice of sleep tracking and measuring physiological signals in sleep is widely practiced. Therefore, sleep monitoring in both the laboratory and ambulatory environments results in the accrual of massive amounts of data that uniquely positions the field of sleep medicine to gain from AI.

Method: The purpose of this article is to provide a concise overview of relevant terminology, definitions, and use cases of AI in sleep medicine. This was supplemented by a thorough review of relevant published literature.

Results: Artificial intelligence has several applications in sleep medicine including sleep and respiratory event scoring in the sleep laboratory, diagnosing and managing sleep disorders, and population health. While still in its nascent stage, there are several challenges which preclude AI's generalizability and wide-reaching clinical applications. Overcoming these challenges will help integrate AI seamlessly within sleep medicine and augment clinical practice.

Conclusion: Artificial intelligence is a powerful tool in healthcare that may improve patient care, enhance diagnostic abilities, and augment the management of sleep disorders. However, there is a need to regulate and standardize existing machine learning algorithms prior to its inclusion in the sleep clinic.

Keywords: Artificial intelligence; Disorders of excessive somnolence; Machine learning; Polysomnogram; Sleep apnea.

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Conflict of interest statement

Author Anuja Bandyopadhyay declares that she has no conflict of interest. Author Cathy Goldstein is on the medical advisor boards of Huxley medical and eviCore. She receives royalties from UpToDate. She is 5% inventor of a circadian mobile application licensed to Arcascope, LLC.

Figures

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Fig. 1
Artificial Intelligence timeline
Fig. 2
Fig. 2
Types of machine learning
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Fig. 3
Development of machine algorithms
See this image and copyright information in PMC

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