AASM Committees Present: AI Uncovered: The Future of Sleep Medicine Education-AASM Committees Present: AI Uncovered: The Future of Sleep Medicine Education

Video Transcription

Video Summary

The webinar “AI Uncovered: The Future of Sleep Medicine Education” explored how artificial intelligence is reshaping sleep medicine practice, training, and fellowship selection. Panelists discussed AI’s near-term impact on auto-scoring sleep studies, telehealth efficiency, patient intake, chatbots for insomnia, wearable-device data interpretation, and improved decision support for disorders such as narcolepsy, REM behavior disorder, and obstructive sleep apnea. They emphasized AI’s potential to improve diagnosis, phenotype disease, predict treatment response, and support personalized care.<br /><br />A major theme was education: fellows and sleep technologists must learn not only how to use AI, but how to evaluate it, including validation, generalizability, explainability, privacy, and bias. Speakers stressed that AI should function as an assistant, not a replacement, and that human oversight remains essential. They also warned that overreliance on AI could weaken core clinical and scoring skills if trainees lose curiosity about raw data and signals.<br /><br />The panel also addressed fellowship applications, noting that AI may generate more polished but less authentic personal statements and could introduce bias if used for screening. Structured behavioral interviews and human review were recommended to preserve equity and authenticity.<br /><br />Finally, the discussion highlighted serious concerns about data privacy, prompt leakage, model inversion attacks, hallucinations, and legal responsibility. The panel concluded that AI adoption should be gradual, transparent, and guided by human expertise, with strong training, institutional safeguards, and ongoing AASM educational resources.

Keywords

artificial intelligence

sleep medicine

auto-scoring

telehealth

insomnia chatbots

wearable data

narcolepsy

obstructive sleep apnea

AI education

data privacy