Incorporating AI Can Support in Advancing Treatment Options

With our lifestyle getting more hectic day by day, facing a sleep issue is a common thing today. Sleep medicine is a field completely devoted to perform research and development to cure such sleep disorders. Researchers performed a study to find out how advanced technologies such as artificial intelligence (AI) can aid in treating sleep issues.

AI to Improve Precision in Sleep Medicine

Scientists from AASM’s Artificial Intelligence in Sleep Medicine Committee highlighted that  implementation of AI can improve the precision and efficiency in sleep medicine. In new position statement, they emphasized that this research will aid in better outcomes and patient-centered treatment plans. The research is available for access in the Journal of Clinical Sleep Medicine.

Dr. Cathy Goldstein worked as the lead author of this study. She stated, “When typically thinking of AI use in sleep medicine, scoring of sleep and related events is one obvious use case. The latest research would aid in streamlining the sleep laboratories’ processes. In addition, AI will decrease some burden on sleep technologists and allow them to use some additional time for direct patient assistance.”

Generally, sleep centers gather a huge amount of data. Use of machine learning and AI for this work can assist in more precise diagnosis. Moreover, it can also advance the disease prediction and treatment prognosis process along with disease subtypes’ characterization. As a result, AI will advance the overall sleep care by offering precision in sleep scoring and sleep treatment personalization and optimization.

According to Goldstein, the use of AI can help to automate sleep scoring. In addition to this, it can spot additional insights from sleep data. She stated, “AI might assist us comprehend mechanisms causing obstructive sleep apnea. This can help us to select the precise treatment option for the right patient at the right time. Moreover, it will offer us better options than the trial and error or one-size-fits-all approaches.”