Advancements, challenges, and prospects of explainable AI in sleep disordered breathing

algorithms for automatic sleep staging, using e.g., neural networks, which carry the benefit of being able to process raw signal as input [1].Despite promising advancements in certain areas of medicine, and the evident versatility of such applications, the clinical utility of AI within the healthcare sector remains limited [2].In sleep medicine, implementation of AI has been assessed for screening/ diagnosis, clinical decision-making, and patient communication [3,4].For example, fine-tuned large language models (LLMs) have been shown to outperform clinicians in generating differential diagnoses for challenging clinical cases [5], and various ML algorithms have been successfully trained to diagnose/classify specific diseases [6][7][8].Novel meaningful subtypes may be discovered using ML by enabling automation of various processes, such as sleep study scoring and analysis of complex multiomics data [9].Similarly, ML can further the efforts to provide personalized and data-driven management, exemplified by high prediction accuracy in modern algorithms [3].These technologies may also be used for "soft" tasks, such as patient education by distilling complex medical