New York: Not just overnight sleep tests, Artificial intelligence (AI) too has the potential to improve efficiencies and precision in sleep medicine, resulting in a more patient-centered care and better outcomes, researchers have found. The electrophysiological data collected during polysomnography — the most comprehensive type of sleep study — is well-positioned for enhanced analysis through […]
New York: Not just overnight sleep tests, Artificial intelligence (AI) too has the potential to improve efficiencies and precision in sleep medicine, resulting in a more patient-centered care and better outcomes, researchers have found.
The electrophysiological data collected during polysomnography -- the most comprehensive type of sleep study -- is well-positioned for enhanced analysis through AI and machine-assisted learning, according to a new position statement from the American Academy of Sleep Medicine.
"When we typically think of AI in sleep medicine, the obvious use case is for the scoring of sleep and associated events," said Cathy Goldstein, associate professor of sleep medicine and neurology at the University of Michigan.
"This would streamline the processes of sleep laboratories and free up sleep technologist time for direct patient care."
Because of the vast amounts of data collected by sleep centres, AI and machine learning could advance sleep care, resulting in a more accurate diagnosis, prediction of disease and treatment prognosis.
"AI could allow us to derive more meaningful information from sleep studies, given that our current summary metrics, for example, the apnea-hypopnea index, aren't predictive of the health and quality of life outcomes that are important to patients," elaborated Goldstein.
"Additionally, AI might help us understand mechanisms underlying obstructive sleep apnea, so we can select the right treatment for the right patient at the right time, as opposed to one-size-fits-all or trial and error approaches," she added in a paper published in the Journal of Clinical Sleep Medicine.
Important considerations for the integration of AI into the sleep medicine practice include transparency and disclosure, testing on novel data, and laboratory integration.
AI tools hold great promise for medicine in general, but there has also been a great deal of hype, exaggerated claims and misinformation.
"We want to interface with industry in a way that will foster safe and efficacious use of AI software to benefit our patients. These tools can only benefit patients if used with careful oversight," the authors wrote.