Abstract
Obstructive sleep apnea (OSA) represents a significant and increasingly prevalent health burden, impacting individual patients through diminished quality of life, increased morbidity and mortality, as well as society at large, via reduced productivity and escalating healthcare and welfare expenditures. As a multifactorial and heterogeneous disorder, OSA encompasses diverse endotypes and phenotypes, necessitating personalized approaches to diagnosis and management in order to achieve optimal clinical outcomes. Modern telemedicine encompasses a broad spectrum of digital tools designed to enhance the efficiency and precision of care delivery for complex conditions. Recent years have witnessed the rapid integration of advanced telehealth technologies, including consumer-grade devices, into clinical practice. Simultaneously, artificial intelligence (AI) has emerged as a transformative force in healthcare, enabling the automation of routine tasks, advanced data analytics, and the generation of novel clinical hypotheses. Within this domain, large language models, a subclass of AI specializing in natural language processing, offer new opportunities for augmenting patient-provider interactions, including streamlining communication and triaging patient-reported data. Despite these technological advancements, the full potential of telemedicine in the management of OSA remains underexplored. However, its implementation is expanding, particularly in longitudinal care models involving large patient cohorts. This Perspective aims to synthesize current state-of-the-art developments and proposes a comprehensive, integrated framework that leverages telemedicine, AI, and a multidimensional understanding of comorbidities and treatable traits throughout the continuum of OSA care, from screening and diagnosis to adherence monitoring and treatment optimization.