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Clinical applications of artificial intelligence in sleep medicine: a sleep clinician’s perspective
Open accessAbstract
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.
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“Advanced signal processing algorithms, including ML and AI, have enabled better differentiation between sleep stages and subtle changes in sleep patterns [240].”
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Metadata
- Authors
- Bandyopadhyay, A., & Goldstein, C.
- Affiliations
- Indiana University Indianapolis (US)
- University of Michigan (US)
- Corresponding author
- Bandyopadhyay, Anuja
- Venue
- Sleep And Breathing
- Published
- 2022-03-09
- Volume
- 27
- Issue
- 1
- Pages
- 39-55
- ISSN
- 1520-9512 (ISSN-L)
- 1522-1709
- PMC
- PMC8904207
- OpenAlex
- W4220995359
- Cited by
- 108
- References
- 97
- Citations by year
- 2026: 24 · 2025: 29 · 2024: 23 · 2023: 28 · 2022: 4
- FWCI
- 3.08
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Sleep And Breathing (publishedVersion, other-oa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- IUScholarWorks (Indiana University) (submittedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Health Sciences › Medicine › Physiology (medical) › Obstructive Sleep Apnea Research
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial intelligence, sleep medicine, sleep monitoring, sleep disorder diagnosis, sleep disorder management, machine learning, population health, clinical implementation barriers
- MeSH terms
- Machine Learning, Algorithms, Artificial Intelligence, Humans, Sleep, Sleep Wake Disorders
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