AI has the capacity to enhance effectiveness and meticulousness in sleep medicine, not just overnight sleep tests. Because of this, researchers have found a more patient-centered way of treatment with better outcomes.
Cathy Goldstein, associate professor of sleep medicine and neurology at the University of Michigan, elaborated,
“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.”
Goldstein commented, “When we typically think of AI in sleep medicine, the obvious use case is for the scoring of sleep and associated event.”
In a paper published in the Journal of Clinical Sleep Medicine, she mentioned,
“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. Important considerations for the integration of AI into the sleep medicine practice include transparency and disclosure, testing on novel data, and laboratory integration.”
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