{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T10:02:58Z","timestamp":1789984978597,"version":"4.0.1"},"reference-count":91,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T00:00:00Z","timestamp":1779667200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T00:00:00Z","timestamp":1779667200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Health Science Reports"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Machine learning (ML) has emerged as a transformative tool in sleep medicine, offering innovative solutions for diagnosing, treating, and understanding sleep disorders. This review examines the current state and future potential of ML applications in this domain.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a comprehensive review of the literature, focusing on key advancements in ML technologies applied to sleep medicine. Specific areas explored include the classification of sleep stages, treatment optimization for sleep apnea and insomnia, and the integration of wearable technology for monitoring sleep patterns.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>ML has significantly improved the accuracy and efficiency of sleep stage classification and enabled the development of tailored treatments for sleep disorders. Wearable technology has facilitated real\u2010time, non\u2010invasive sleep monitoring. Despite these advancements, challenges such as data privacy concerns, ethical considerations, and the need for interdisciplinary collaboration remain substantial barriers to broader adoption.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Machine learning has the potential to revolutionize sleep medicine by personalizing care and enhancing predictive models for managing sleep disorders. Overcoming existing challenges is crucial to fully realize the benefits of ML, enabling more effective and personalized approaches to diagnosis and treatment. This review highlights the transformative role of ML in sleep medicine and its capacity to improve sleep health outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/hsr2.72323","type":"journal-article","created":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T07:47:42Z","timestamp":1779695262000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Revolutionizing Sleep Medicine: The Impact of Machine Learning on Diagnosis, Treatment, and Personalized Care"],"prefix":"10.1002","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4970-4968","authenticated-orcid":false,"given":"Alexander A.","family":"Huang","sequence":"first","affiliation":[{"name":"Cornell University Ithaca New York USA"},{"name":"School of Medicine Northwestern University Feinberg Chicago Illinois USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel 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