Big Data and Artificial Intelligence in Sleep Medicine: Opportunities and Obstacles
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Arash Maghsoudi1, Ju Lynn Ong2, Javad Razjouyan3
1Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, TX, USA; Department of Medicine, Baylor College of Medicine, Houston, TX, USA.
Abstract:
This review examines the transformative role of big data and artificial intelligence (AI) in modern sleep medicine. It outlines the evolution from traditional rule-based sleep staging to advanced machine learning, deep learning, and emerging foundation and agent-based models. The article highlights diverse data sources and discusses the challenges of data quality, harmonization, privacy, and governance. The review also addresses real-world implementation, stressing the importance of human-in-the-loop systems, interoperability, and ongoing validation. Finally, it considers regulatory, ethical, and economic factors, concluding that while AI holds great promise, its success depends on rigorous validation, transparent governance, and equitable, clinically meaningful deployment.
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