Data Sheet 1_Artificial intelligence for sleep-disordered breathing: a taxonomy of methods, data modalities, and clinical applications.pdf

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.