Artificial Intelligence in the Management of Obstructive Sleep Apnea: Innovations and Challenges
Recommended Citation
Kaspo G. Artificial Intelligence in the Management of Obstructive Sleep Apnea: Innovations and Challenges. Sleep Med 2026; 138.
Document Type
Conference Proceeding
Publication Date
2-1-2026
Publication Title
Sleep Med
Abstract
Introduction: Obstructive Sleep Apnea (OSA) is a prevalent and underdiagnosed disorder that significantly impacts cardiovascular health, cognitive function, and overall quality of life. Traditional diagnostic and treatment pathways for OSA—such as polysomnography and CPAP therapy—face challenges related to accessibility, cost, patient adherence, and clinical resource constraints. In this context, artificial intelligence (AI) emerges as a transformative tool with the potential to revolutionize every stage of OSA management. From automating early screening in primary care and dental settings to enhancing the accuracy of diagnostic algorithms and optimizing personalized treatment plans, AI is reshaping how clinicians approach this complex condition. Yet, while AI-driven solutions offer tremendous promise, they also raise important questions around data privacy, algorithm transparency, clinical validation, and integration into existing care models. This review explores the current innovations, applications, and limitations of AI in OSA management, emphasizing both the clinical potential and the ethical, technical, and operational challenges that must be addressed to realize its full impact. Materials and methods: This review was conducted through a comprehensive search of peer-reviewed literature and clinical studies published between 2015 and 2025, focusing on the application of artificial intelligence (AI) in the diagnosis and management of Obstructive Sleep Apnea (OSA). Databases searched included PubMed, Scopus, and IEEE Xplore using keywords such as "artificial intelligence," "machine learning," "sleep apnea," "OSA screening," and "AI diagnosis." Included studies were selected based on relevance to AI-driven innovations in early screening, diagnostic accuracy, treatment personalization, and long-term management of OSA. Articles addressing ethical, technical, and integration challenges of AI in clinical practice were also reviewed to provide a balanced perspective. The findings were categorized into thematic areas: (1) AI in screening and early detection; (2) AI-supported diagnostic tools; (3) AI-guided treatment planning; and (4) system-level challenges including data privacy, algorithm bias, and implementation barriers. The review aims to synthesize current knowledge and identify gaps for future research. Results: AI applications in OSA management showed promising outcomes across four areas: 1. Screening – AI tools effectively identified at-risk individuals using health records, questionnaires, and wearable data. 2. Diagnosis – Machine learning models improved accuracy in detecting sleep events and apnea severity. 3. Treatment – AI helped personalize therapies by predicting CPAP adherence and matching patients to alternatives. 4. Challenges – Key barriers include limited clinical validation, data privacy concerns, and poor integration into care systems. Overall, AI shows strong potential but requires further refinement for routine clinical use. Conclusions: Artificial intelligence is reshaping the landscape of obstructive sleep apnea management by enhancing screening, diagnosis, and personalized treatment. While early results are promising, widespread clinical adoption remains limited by challenges in validation, data security, and system integration. Continued research and cross-disciplinary collaboration are essential to fully realize AI’s potential in improving OSA care.
Volume
138
