arXiv:2602.07628v2 Announce Type: replace-cross Abstract: While the shift toward unified foundation models has revolutionized many deep learning domains, sleep medicine remains largely restricted to task-specific models that focus on localized micro-structure features. These approaches often neglect the rich, multi-modal context of Polysomnography (PSG) and fail to capture the global macro-structure of a full night's sleep. To address this, we introduce SleepMaMi , a Sleep Foundation Model engineered to master both hour-long sleep architectures and fine-grained signal morphologies. Our framewo
The proliferation of foundation models in other domains makes their application to complex, multi-modal biological data like sleep medicine a natural progression, enabled by increasing compute capabilities.
This development indicates the growing trend of applying general-purpose AI models to specialized scientific and medical fields, potentially yielding breakthroughs where traditional methods have stagnated.
The shift from task-specific models to a 'universal sleep foundation model' fundamentally changes how sleep disorders might be diagnosed, understood, and treated, moving towards more holistic patient data analysis.
- · AI researchers in specialized domains
- · Sleep diagnostic companies adopting foundation models
- · Patients with complex sleep disorders
- · Healthcare data analytics platforms
- · Developers of legacy task-specific sleep models
- · Healthcare providers resistant to AI integration
Improved accuracy and efficiency in sleep disorder diagnosis and research.
Development of personalized sleep interventions based on comprehensive macro-micro sleep structure analysis.
Integration of sleep foundation models with other health AI systems to create holistic digital twins for proactive health management.
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