Stanford Medicine reports that researchers built SleepFM, an AI foundation model trained on about 585,000 hours of polysomnography (sleep study) data. The model predicts future disease risk across around 130 outcomes, including dementia, Parkinson’s disease, cardiovascular disease, cancer, and even death risk.
It works by analyzing a single night of sleep patterns and extracting hidden health signals that traditional scoring misses.
This is important because it signals a shift toward “passive” preventative medicine where routine sleep data may act like a long-term health biomarker. It could enable earlier interventions and more personalized risk monitoring.
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