Talking Sleep Artificial Intelligence in Sleep Medicine
Apr 8, 2021
In this engaging discussion, Dr. Anuja Bandyopadhyay, a sleep medicine physician and member of the AASM's Artificial Intelligence in Sleep Medicine Committee, shares insights on the transformative impact of AI in sleep medicine. She delves into AI-assisted scoring and personalized treatment plans, highlighting its potential to revolutionize diagnosis of sleep apnea. The conversation covers the importance of standardizing AI algorithms and the role of innovative technologies in improving patient care, emphasizing a collaborative future in healthcare.
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AI Learns Sleep Scoring Rules
- AI and machine learning score sleep studies by learning from vast examples rather than fixed rules.
- This approach can detect events like apnea without manually defined thresholds, reflecting decades of expert scoring.
Deep Learning Extracts Raw Data Features
- Deep learning extracts features directly from raw sleep data unlike traditional machine learning.
- This allows AI to find complex sleep patterns without manual input, enhancing analysis capabilities.
Set Standards Before AI Use
- Sleep labs should demand AI algorithms be transparent, generalizable, and backed by clear policies.
- Such standards are prerequisites before adopting AI-assisted sleep scoring clinically.