Abstract
Advances in sleep medicine have driven significant improvements in the diagnosis and treatment of sleep disorders such as obstructive sleep apnea (OSA). This disorder affects one billion people worldwide and traditionally, diagnosis is based on polysomnography (PSG), a laborious method that requires specialized personnel. However, the integration of artificial intelligence (AI) in sleep medicine has made it possible to automate the analysis of sleep phases and respiratory events with high accuracy.Machine learning algorithms and neural networks have proven to be effective in automatic sleep coding, with hit rates comparable to those of human experts. These advances make it possible to improve the efficiency of sleep labs and to personalize OSA treatment. In addition, techniques such as cluster analysis are used to identify symptomatic patterns and phenotypes, which improves understanding of OSA pathophysiology and optimizes CPAP treatment.However, implementation of AI in hospitals faces technological, ethical, and legal barriers. Challenges include data quality, patient privacy, and the need for specialized personnel. Despite these obstacles, AI and Big Data have the potential to transform medical care for sleep disorders, improving both diagnosis and treatment adherence, provided regulatory and cultural barriers are overcome.