Researchers unveiled a foundation model aimed at turning sleep signals into an early-warning system for health risks and clinical outcomes. The approach focuses on learning from measurable physiological patterns generated during sleep—covering signals across the brain, heart, breathing, muscles, and nervous system—to support risk prediction and downstream outcome assessment. The concept aligns with the broader shift toward using multimodal health data from wearable or sensor-based inputs rather than relying solely on episodic clinical measurements. As AI models become increasingly capable of extracting structured patterns from complex time-series data, sleep may move closer to functioning as a window for proactive diagnostics. If validated in prospective clinical settings, sleep-based foundation modeling could reduce friction for early screening and help prioritize who needs confirmatory testing.