What if one night in a sleep lab could offer a glimpse into your long-term health? Researchers are now using a foundation model trained on hundreds of thousands of hours of sleep data to do just that, by predicting the next five seconds of a polysomnogram, the model learns the rhythms of sleep and, with minimal fine-tuning, begins estimating risks for conditions like Parkinson’s, dementia, heart failure, stroke, and even some cancers.

We break down how it works: during a sleep study, sensors capture brain waves (EEG), eye movements (EOG), muscle tone (EMG), heart rhythms (ECG), and breathing. The model compresses these multimodal signals into a reusable format, much like how language models process text. Add a small neural network, and suddenly those sleep signals can help predict disease risk up to six years out. The associations make clinical sense: EEG patterns are more telling for neurodegeneration, respiratory signals flag pulmonary issues, and cardiac rhythms hint at circulatory problems. But, the scale of what’s possible from a single night’s data is remarkable.

We also tackle the practical and ethical questions. Since sleep lab patients aren’t always representative of the general population, we explore issues of selection bias, fairness, and external validation. Could this model eventually work with consumer wearables that capture less data but do so every night? And what should patients be told when risk estimates are uncertain or only partially actionable?

If you're interested in sleep science, AI in healthcare, or the delicate balance of early detection and patient anxiety, this episode offers a thoughtful look at what the future might hold—and the trade-offs we’ll face along the way.

Reference:

A multimodal sleep foundation model for disease prediction
Rahul Thapa
Nature (2026)

Credits:

Theme music: Nowhere Land, Kevin MacLeod (incompetech.com)
Licensed under Creative Commons: By Attribution 4.0
https://creativecommons.org/licenses/by/4.0/