A poor night’s sleep portends a bleary-eyed next day, but it could also hint at diseases that will strike years down the road. A new artificial intelligence model developed by Stanford Medicine researchers and their colleagues can use physiological recordings from one night’s sleep to predict a person’s risk of developing more than 100 health conditions.

Known as Sleep FM, the model was trained on nearly 600,000 hours of sleep data collected from 65,000 participants. The sleep data comes from polysomnography, a comprehensive sleep assessment that uses various sensors to record brain activity, heart activity, respiratory signals, leg movements, eye movements and more.

Polysomnography is the gold standard in sleep studies that monitor patients overnight in a lab. It is also, the researchers realized, an untapped gold mine of physiological data.

“We record an amazing number of signals when we study sleep,” said Emmanuel Mignot, MD, PhD, the Craig Reynolds Professor in Sleep Medicine and co-senior author of the new study, which published Jan. 6 in Nature Medicine. “It’s a kind of general physiology that we study for eight hours in a subject who’s completely captive. It’s very data rich.”

Only a fraction of that data is used in current sleep research and sleep medicine. With advances in artificial intelligence, it’s now possible to make sense of much more of it. The new study is the first to use AI to analyze such large-scale sleep data.

“From an AI perspective, sleep is relatively understudied. There’s a lot of other AI work that’s looking at pathology or cardiology, but relatively little looking at sleep, despite sleep being such an important part of life,” said James Zou, PhD, associate professor of biomedical data science and co-senior author of the study.

Source: Stanford medicine - January 6, 2026