Researchers at the Icahn School of Medicine have developed a powerful AI tool, built on the same transformer architecture used by large language models like ChatGPT, to process an entire night’s sleep. To date, it is one of the largest studies, analyzing 1,011,192 hours of sleep. The model, called patch foundational transformer for sleep (PFTSleep), analyzes brain waves, muscle activity, heart rate, and breathing patterns to classify sleep stages more effectively than traditional methods, streamlining sleep analysis, reducing variability, and supporting future clinical tools to detect sleep disorders and other health risks. 

Current sleep analysis often relies on human experts manually scoring short segments of sleep data or using AI models that are not capable of analyzing a patient’s entire night of sleep. This new approach, developed using thousands of sleep recordings, takes a more comprehensive view. By training on full-length sleep data, the model can recognize sleep patterns throughout the night and across different populations and settings, offering a standardized and scalable method for sleep research and clinical use, say the investigators. 

This is a step forward in AI-assisted sleep analysis and interpretation, says first author Benjamin Fox, a PhD candidate at the Icahn School of Medicine at Mount Sinai in the Artificial Intelligence and Emerging Technologies Training Area. By leveraging AI in this way, we can learn relevant clinical features directly from sleep study signal data and use them for sleep scoring and, in the future, other clinical applications such as detecting sleep apnea or assessing health risks linked to sleep quality.

This AI-driven approach has the potential to revolutionize sleep research, says co-senior corresponding author Girish N. Nadkarni, MD, MPH, Chair of the Windreich Department of Artificial Intelligence and Human Health at the Icahn School of Medicine, Director of the Hasso Plattner Institute for Digital Health, and the Irene and Dr. Arthur M. Fishberg Professor of Medicine. Dr. Nadkarni is also the inaugural Chief of the Division of Data-Driven and Digital Medicine and Co-Director of the Mount Sinai Clinical Intelligence Center. By analyzing entire nights of sleep with greater consistency, we can uncover deeper insights into sleep health and its connection to overall well-being.

Source: https://www.mountsinai.org/about/newsroom/2025/new-ai-model-analyzes-full-night-of-sleep-with-high-accuracy-in-largest-study-of-its-kind