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

Rapid advancements in artificial intelligence (AI), in combination with increased availability of rich large-scale clinical data, has paved the way for promising implementations in both diagnosing/subtyping as well as managing of sleep disordered breathing (SDB). A central strength of AI in this regard is how it facilitates analysis of complex multidimensional/modal data, for example pertaining to comorbidities and so-called treatable traits. However, the utility of such applications remains somewhat limited, as most AI models are so-called “black boxes”, for which it is not intuitive to assess how an output was arrived at. This poses challenges for ensuring patient trust and controlling bias. Explainable AI (XAI), which constitutes techniques to either distill “black box” models to simpler ones or to elucidate influential patterns by surrogate “white box” models, may provide such insights. Adoption of XAI within this field, however, is still at a nascent stage, and overall, the optimal role of AI alongside clinicians continues to be unclear. This review provides a clinically oriented overview of state-of-the-art implementations of AI and XAI in SDB. In addition, we discuss limitations and trade-offs with XAI and propose a general framework for personalized management of obstructive sleep apnea using XAI and treatable traits.