arXiv:2603.00190v2 Announce Type: replace-cross Abstract: Polysomnography (PSG) provides the gold standard for sleep assessment but suffers from substantial heterogeneity across recording devices and cohorts. There have been growing efforts to build general-purpose foundation models (FMs) for sleep physiology, but lack an in-depth understanding of the pre-training process and scaling patterns that lead to more generalizable sleep FMs. To fill this gap, we curate a massive corpus of 166,500 hours of sleep recordings from nine public sources and establish SleepBench, a comprehensive, fully open-
The proliferation of advanced AI techniques and the increasing availability of specialized biological data are enabling the creation of foundation models for complex physiological analyses like sleep.
This development represents a significant step towards general-purpose foundation models in healthcare, offering unbiased and scalable analysis critical for both research and clinical applications.
The ability to accurately pre-train and scale sleep foundation models will standardize sleep assessment, reduce heterogeneity across studies, and accelerate personalized sleep medicine.
- · AI researchers in healthcare
- · Sleep diagnostic companies
- · Pharmaceutical companies developing sleep aids
- · Chronic disease management platforms
- · Traditional, manual sleep analysis methods
- · Companies relying on proprietary, siloed sleep datasets
Standardized and more accessible sleep diagnostics will improve patient outcomes and resource allocation in healthcare.
The successful scaling of sleep foundation models could pave the way for similar FMs in other physiological domains, accelerating biological research.
These models might eventually integrate with wearable tech and AI agents to offer proactive, personalized health interventions, blurring the lines between clinical care and lifestyle management.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI