Artificial intelligence for sleep-disordered breathing: a taxonomy of methods, data modalities, and clinical applications

Abstract

Sleep-disordered breathing (SDB), particularly obstructive sleep apnea (OSA), is a highly prevalent group of disorders associated with disrupted sleep, intermittent hypoxemia, excessive daytime sleepiness, neurocognitive impairment, and cardiometabolic morbidity. Although laboratory polysomnography remains central to diagnosis, conventional sleep testing can be costly, labor-intensive, and difficult to scale. Recent advances in data mining, machine learning, deep learning, wearable sensing, mobile health, multimodal analytics, and large language models are creating new opportunities for more accessible, personalized, and efficient SDB care. This review examines recent advances in artificial intelligence for SDB diagnosis and management, with emphasis on polysomnography and home sleep apnea testing, wearable and contactless screening, audio and physiological-signal analysis, imaging and anatomical risk prediction, phenotyping and endotyping, treatment monitoring, continuous positive airway pressure adherence, and clinically deployable explainable AI. We also discuss the emerging role of large language models in patient education, sleep-report summarization, clinical documentation, and decision-support workflows. Finally, we identify major challenges related to data heterogeneity, external validation, interpretability, privacy, fairness, reproducibility, regulation, and real-world integration into sleep medicine. This review aims to provide an evidence-based framework for researchers and clinicians to understand current AI applications and identify future directions for precision diagnosis and management of SDB.