Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2024 Jul 1;20(7):1183-1191.
doi: 10.5664/jcsm.11132.

Strengths, weaknesses, opportunities, and threats of using AI-enabled technology in sleep medicine: a commentary

Anuja Bandyopadhyay  1 , Margarita Oks  2 , Haoqi Sun  3 , Bharati Prasad  4 , Sam Rusk  5 , Felicia Jefferson  6 , Roneil Gopal Malkani  7   8 , Shahab Haghayegh  9 , Ramesh Sachdeva  10 , Dennis Hwang  11 , Jon Agustsson  12 , Emmanuel Mignot  13 , Michael Summers  14 , Daniel Fabbri  15 , Maryann Deak  16 , Matthew Anastasi  17 , Andrew Sampson  17 , Steve Van Hout  17 , Azizi Seixas  18
Affiliations
Review

Strengths, weaknesses, opportunities, and threats of using AI-enabled technology in sleep medicine: a commentary

Anuja Bandyopadhyay et al. J Clin Sleep Med. .

Abstract

Over the past few years, artificial intelligence (AI) has emerged as a powerful tool used to efficiently automate several tasks across multiple domains. Sleep medicine is perfectly positioned to leverage this tool due to the wealth of physiological signals obtained through sleep studies or sleep tracking devices and abundance of accessible clinical data through electronic medical records. However, caution must be applied when utilizing AI, due to intrinsic challenges associated with novel technology. The Artificial Intelligence in Sleep Medicine Committee of the American Academy of Sleep Medicine reviews advancements in AI within the sleep medicine field. In this article, the Artificial Intelligence in Sleep Medicine committee members provide a commentary on the scope of AI technology in sleep medicine. The commentary identifies 3 pivotal areas in sleep medicine that can benefit from AI technologies: clinical care, lifestyle management, and population health management. This article provides a detailed analysis of the strengths, weaknesses, opportunities, and threats associated with using AI-enabled technologies in each pivotal area. Finally, the article broadly reviews barriers and challenges associated with using AI-enabled technologies and offers possible solutions.

Citation: Bandyopadhyay A, Oks M, Sun H, et al. Strengths, weaknesses, opportunities, and threats of using AI-enabled technology in sleep medicine: a commentary. J Clin Sleep Med. 2024;20(7):1183-1191.

Keywords: artificial intelligence; sleep medicine.

PubMed Disclaimer

Conflict of interest statement

Azizi Seixas reports being a consultant to Idorsia, Philips, and serving on the Board of Directors for Moshi Kids. Emmanuel Mignot reports being a consultant for Takeda Development Center Americas, Inc, Idorsia, Ambulatory Monitoring, and Avadel Pharmaceuticals, Inc (USA). Shahab Haghayegh reports receiving consulting fees from Achaemenid LLC. Jon Agustsson is an employee of Nox Medical ehf. Sam Rusk is an employee and shareholder of EnsoData, Inc. Steve Van Hout, Matthew Anastasi, and Andrew Sampson are employed by the American Academy of Sleep Medicine. The other authors report no conflicts of interest.

Figures

Figure 1
Figure 1. Four domains for application of AI as a tool to improve sleep population health.
AI = artificial intelligence, EHR = electronic health record, HSAT = home sleep apnea test.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Goldstein CA , Berry RB , Kent DT , et al . Artificial intelligence in sleep medicine: an American Academy of Sleep Medicine position statement . J Clin Sleep Med. 2020. ; 16 ( 4 ): 605 – 607 . - PMC - PubMed
    1. Bandyopadhyay A , Goldstein C . Clinical applications of artificial intelligence in sleep medicine: a sleep clinician’s perspective . Sleep Breath. 2023. ; 27 ( 1 ): 39 – 55 . - PMC - PubMed
    1. Lechat B , Scott H , Naik G , et al . New and emerging approaches to better define sleep disruption and its consequences . Front Neurosci. 2021. ; 15 : 751730 . - PMC - PubMed
    1. Stephansen JB , Olesen AN , Olsen M , et al . Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy . Nat Commun. 2018. ; 9 ( 1 ): 5229 . - PMC - PubMed
    1. Brink-Kjaer A , Gunter KM , Mignot E , During E , Jennum P , Sorensen HB . End-to-end deep learning of polysomnograms for classification of REM sleep behavior disorder. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Glasgow, Scotland, United Kingdom, IEEE; ; 2022. : 2941 – 2944 . - PubMed

LinkOut - more resources