Use of Artificial Intelligence in Analysing Recent Trents in Sleep Disorder: A Comprehensive Review

Bingu Shiv kiran Reddy, B. Ghewade, U. Jadhav, Sameer Adwani, Vivek D Alone

TL;DR: The current comprehensive review emphasises the transformative role of AI in sleep medicine, highlighting its capacity to enhance patient quality of life, optimise management strategies and improve diagnostic accuracy.

Sleep disorders affect a large proportion of the population and are traditionally diagnosed using nocturnal polysomnography; however, they are laborious and expensive. Emerging Artificial Intelligence (AI) has modified the diagnosis and disease management strategies for sleep disorders. Moreover, machine learning and deep learning algorithms may precisely recognise disorders including narcolepsy, insomnia, and Obstructive Sleep Apnoea (OSA) utilising intricate electrophysiological information. These modalities aid in evaluating sleep patterns, thereby facilitating earlier diagnosis and tailored management. Integration of the Internet of Medical Things (IoMT) with peripheral devices enables real-time monitoring of past clinical settings. AI-driven tools optimise clinical workflows, minimise healthcare costs, and reduce dependency on manual assessment. Moreover, AI improves clinical outcomes and treatment adherence by enabling accurate phenotyping and personalised treatment modifications. Despite requiring specific procedures, unpredictable sensor information, data privacy issues, algorithm robustness and regulatory compliance are under investigation. The current comprehensive review emphasises the transformative role of AI in sleep medicine, highlighting its capacity to enhance patient quality of life, optimise management strategies and improve diagnostic accuracy.