Awakening Sleep Medicine: The Transformative Role of Artificial Intelligence in Sleep Health

Sleep medicine focuses on diagnosing and managing disorders like obstructive sleep apnea (OSA), which affects nearly one billion people worldwide, yet remains underdiagnosed in many cases. This review explores how artificial intelligence (AI) and machine learning (ML) can address these gaps by improving diagnostic tools, enhancing treatment strategies, and advancing patient outcomes. We also examine key challenges, including data privacy concerns, algorithmic bias, and the lack of standardized methods for integrating AI into clinical practice, while highlighting recent advancements and the evolving role of AI in sleep healthcare.

Recent advancements in AI for sleep medicine include the use of deep learning models for automated sleep stage classification, wearable devices for real-time sleep monitoring, and AI-powered tools that increase the accuracy of home testing for disorders like OSA. ML algorithms enhance diagnostic accuracy by analyzing large datasets to identify patterns that traditional methods might miss. These advancements also enable more precise endotyping, allowing tailored treatment based on specific physiological drivers of sleep disorders, such as airway collapsibility or ventilatory control abnormalities. In the realm of pharmacology, AI is also helping predict drug efficacy, repurpose existing medications for sleep-related disorders, and explore how genetic variations influence individual responses to treatment. These innovations offer new opportunities for personalized medicine in sleep healthcare.

AI holds immense promise in revolutionizing sleep medicine by refining diagnostic tools, optimizing management protocols, and reducing healthcare costs. Recent advancements, such as deep learning models for sleep stage classification, wearable devices for real-time monitoring, and AI-driven pharmacological insights, highlight its potential to transform patient care. Addressing the challenges of data standardization, ethical implementation, and clinician education will be critical to unlocking its full potential. With continued advancements, AI can provide more personalized and efficient care, improving outcomes for patients with sleep-related disorders.