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

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.

Smart sleep: what to consider when adopting AI-enabled solutions in clinical practice of sleep medicine.

Since the publication of its 2020 position statement on artificial intelligence (AI) in sleep medicine by the American Academy of Sleep Medicine, there has been a tremendous expansion of AI-related software and hardware options for sleep clinicians. To help clinicians understand the current state of AI and sleep medicine, and to further enable these solutions to be adopted into clinical practice, a discussion panel was conducted on June 7, 2022, at the Associated Professional Sleep Societies Sleep Conference in Charlotte, North Carolina. The article is a summary of key discussion points from this session, including aspects of considerations for the clinician in evaluating AI-enabled solutions including but not limited to what steps might be taken both by the Food and Drug Administration and clinicians to protect patients, logistical issues, technical challenges, billing and compliance considerations, education and training considerations, and other unique challenges specific to AI-enabled solutions. Our summary of this session is meant to support clinicians in efforts to assist in the clinical care of patients with sleep disorders utilizing AI-enabled solutions. CITATION: Bandyopadhyay A, Bae C, Cheng H, et al. Smart sleep: what to consider when adopting AI-enabled solutions in clinical practice of sleep medicine. J Clin Sleep Med. 2023;19(10):1823-1833.

Photoplethysmography-new applications for an old technology: a sleep technology review.

Education is integral to the American Academy of Sleep Medicine (AASM) mission. The AASM Emerging Technology Committee identified an important and evolving piece of technology that is present in many of the consumer and clinical technologies that we review on the AASM #SleepTechnology (https://aasm.org/consumer-clinical-sleep-technology/) resource-photoplethysmography. As more patients with sleep tracking devices ask clinicians to view their data, it is important for sleep providers to have a general understanding of the technology, its sensors, how it works, targeted users, evidence for the claimed uses, and its strengths and weaknesses. The focus in this review is photoplethysmography-a sensor type used in the familiar pulse oximeter that is being developed for additional utilities and data outputs in both consumer and clinical sleep technologies. CITATION: Ryals S, Chang A, Schutte-Rodin S, et al. Photoplethysmography-new applications for an old technology: a sleep technology review. J Clin Sleep Med. 2023;19(1):189-195.

Consumer Sleep Technology: An American Academy of Sleep Medicine Position Statement.

ABSTRACT: Consumer sleep technologies (CSTs) are widespread applications and devices that purport to measure and even improve sleep. Sleep clinicians may frequently encounter CST in practice and, despite lack of validation against gold standard polysomnography, familiarity with these devices has become a patient expectation. This American Academy of Sleep Medicine position statement details the disadvantages and potential benefits of CSTs and provides guidance when approaching patient-generated health data from CSTs in a clinical setting. Given the lack of validation and United States Food and Drug Administration (FDA) clearance, CSTs cannot be utilized for the diagnosis and/or treatment of sleep disorders at this time. However, CSTs may be utilized to enhance the patient-clinician interaction when presented in the context of an appropriate clinical evaluation. The ubiquitous nature of CSTs may further sleep research and practice. However, future validation, access to raw data and algorithms, and FDA oversight are needed.

Sleep-disordered breathing in neurologic conditions.

Sleep-related breathing disorder or sleep-disordered breathing (SDB) encompasses central sleep apnea (CSA), obstructive sleep apnea (OSA), and sleep-related hypoventilation or hypoxemic syndromes. SDB is common in neurologic conditions that affect the central and/or peripheral nervous systems. Patients with neurologic conditions are at risk for SDB due to a combination of factors such as muscular weakness, damage to areas of the brain that control respiration, use of sedating medications, and weight gain from limited physical activity. This article discusses recognition and treatment of SDB as important aspects of treating patients with neurologic disease.

Insomnia.

Insomnia is a common disorder, with individual and societal consequences. Advances have been made in the understanding of insomnia and its treatment options. However, cognitive behavioral therapy and Food and Drug Administration-approved pharmacologic therapies have limitations, the former primarily involving access and the latter involving potential side effects. Further research is needed to optimize management strategies.

The role of sleep in forgetting in temporal lobe epilepsy: a pilot study.

The purpose of this study was to examine how sleep impacts memory function in temporal lobe epilepsy (TLE). Patients with TLE (n=7) and control subjects (n=9) underwent training and overnight testing on (1) a motor sequence task known to undergo sleep-dependent enhancement in healthy subjects, and (2) the selective reminding test, a verbal memory task on which patients with TLE have shown impaired performance 24 hours after training. Sleep data were collected by polysomnography. Results indicate that patients with TLE display greater forgetting on the selective reminding test compared with controls over 12 hours of daytime wakefulness, but not over a similar period including a night of sleep. Slow wave sleep is correlated with overnight performance change on the selective reminding test. Patients with TLE show no deficit in sleep-dependent motor sequence task improvement. The findings provide potential insight into the pattern and pathophysiology of forgetting in TLE.

Sleep and cognition.

Sleep is a complex physiologic state, the importance of which has long been recognized. Lack of sleep is detrimental to humans and animals. Over the past decade, an important link between sleep and cognitive processing has been established. Sleep plays an important role in consolidation of different types of memory and contributes to insightful, inferential thinking. While the mechanism by which memories are processed in sleep remains unknown, several experimental models have been proposed. This article explores the link between sleep and cognition by reviewing (1) the effects of sleep deprivation on cognition, (2) the influence of sleep on consolidation of declarative and non-declarative memory, and (3) some proposed models of how sleep facilitates memory consolidation in sleep. Copyright © 2010 John Wiley & Sons, Ltd. For further resources related to this article, please visit the WIREs website.