AI Revolutionizes Early Disease Detection
- STANFORD / LONDON (IT BOLTWISE) - Researchers at Stanford University have developed an AI model that can predict over 130 diseases years in advance based on sleep data.
- Stanford University has achieved a significant advancement in preventative medicine with it's new AI model, SleepFM.
- Traditionally, sleep data has been used primarily to diagnose sleep disorders such as apnea.
STANFORD / LONDON (IT BOLTWISE) – Researchers at Stanford University have developed an AI model that can predict over 130 diseases years in advance based on sleep data. This technology could revolutionize preventative medicine and offers new opportunities for the early detection of diseases such as Parkinson’s,dementia,and cancer.
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Stanford University has achieved a significant advancement in preventative medicine with it’s new AI model, SleepFM. This technology analyzes sleep patterns and can predict over 130 diseases, including Parkinson’s, dementia, and cancer, years before their onset. The researchers used data from nearly 65,000 people collected over several decades to train the model. This progress could fundamentally change the way we practice healthcare.
Traditionally, sleep data has been used primarily to diagnose sleep disorders such as apnea. However, the team led by professors Emmanuel Mignot and James Zou recognized that the recorded brain waves, heart rates, and breathing patterns hold far deeper insights into overall health. SleepFM utilizes a multimodal AI architecture that processes physiological data similarly to speech AIs to gain these insights.
The prediction accuracy of SleepFM is notable. In tests, the model achieved an accuracy of 0.89 in predicting Parkinson’s and 0.85 for dementia.The AI also demonstrated strong predictive power for other diseases such as prostate cancer and breast cancer. These values often exceed the capabilities of today’s clinical tools, which focus on individual data.
To accelerate the dissemination of the technology,the research team has released the code for SleepFM as open-source software on GitHub. This allows other institutions to validate the results and integrate the model into their own datasets. Experts see great potential in integrating this technology into wearables, enabling continuous health monitoring
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