Artificial intelligence in out‐of‐hospital sleep monitoring: Bridging polysomnography and wearable signals

Polysomnography (PSG), the reference standard for sleep assessment, has limited scalability for large‐scale screening, driving the development of wearable and unobtrusive monitors. However, the physiological signals these devices capture are physiologically correlated with PSG channels but are not completely equivalent. Their reliability and validity for clinical use therefore require careful evaluation. The advent of artificial intelligence (AI) provides a technical bridge to close the representation gap between PSG signals and wearable/unobtrusive signals. This review outlines the core role of PSG signals in sleep assessment and sleep disorder diagnosis, including sleep structure analysis, respiratory event detection, and abnormal behavior identification. It then explores the current research progress of wearable and unobtrusive sleep monitoring devices, such as smartwatches, sleep monitoring pads, and radars, for sleep staging and the diagnosis of common sleep disorders such as sleep apnea, and discusses the use of AI in such devices, with a particular focus on building interpretable models to enhance the clinical trustworthiness of out‐of‐hospital sleep monitoring. This article aims to provide valuable insights into the development of low‐burden, easy‐to‐operate, trustworthy assessment tools for out‐of‐hospital sleep monitoring, with the goal of facilitating early detection, personalized management, and long‐term follow‐up of patients with sleep disorders.