Transformative Insights: Unveiling the Potential of Artificial Intelligence in the Treatment of Sleep Disorders - A Comprehensive Review

Disruptions to sleep have a substantial influence on people's overall health and quality of life. The conventional techniques for diagnosing and managing sleep disorders usually rely on subjective assessments and qualitative evaluations, that may have some accuracy and efficacy limitations. Nevertheless, recent developments in the field of artificial intelligence (AI) have presented new opportunities for better diagnosing and treating problems with insomnia. The paper reviews in depth the uses of AI in the domain of medical sleep medicine. We look at the use of algorithmic techniques for deep learning and machine learning for identifying indicators of sleep-related issues, the assessment of sleep quality, sleep tracking, and the establishment of individualized sleep therapeutics. We also discuss how AI is being used to construct forecasting models that may be used to identify individuals who are at risk of experiencing sleep issues and improve treatment strategies. In addition, we talk about the challenges and potential outcomes of incorporating AI-based techniques into clinical practice. Overall, our research highlights how AI has the potential to transform the field of sleeping medicine and improve outcomes for people with sleep-related conditions.

1. Introduction

Sleep problems including apnea and narcolepsy, and sleeplessness affect a high number of individuals and can be harmful to both physical and emotional health. Conventional approaches to recognizing and managing sleep disorders rely on arbitrary judgments, constrained monitoring techniques, and qualitative evaluations, these may be laborious, expensive, and highly susceptible to human error [1]. But recent advances in artificial intelligence (AI) provide new opportunities to revolutionize the field of medical sleep medicine [2]. Algorithms utilizing machine learning and deep learning have shown promise in a variety of healthcare-related domains, including image recognition, machine learning for natural language processing, and healthcare decision-support systems [3]. In terms of accuracy, efficacy, and personalization, these techniques could enhance the identification and management of sleep-related illnesses. Utilizing AI algorithms makes it possible to analyze large amounts of sleep data, recognize patterns, and create prediction models for early detection and intervention [4].