• Artificial Itelligence and the Jaw

  • Sep 24 2024
  • Length: 52 mins
  • Podcast

Artificial Itelligence and the Jaw

  • Summary

  • For nearly a hundred years, science fiction stories have been giving us an idea of what living with artificial intelligence might be like. But we don't have to look to our favorite sci-fi to see artificial intelligence, also called AI, in action. It's already making an impact in our everyday lives whether we realize it or not. When you ask Alexa or Siri a question, unlock your phone using face recognition, or get a notice from your bank about possible fraudulent activity on your account, AI is working in the background to offer us an opportunity or information that we didn't have before. AI uses computers and machines to solve problems and make decisions in the same way human minds do, faster and often with more accuracy. This offers incredible opportunities in biomedicine, where AI can not only help us understand more about how the human body works, it can help us discover the best ways to treat patients, leading to better outcomes.

    In this episode of Science Never Sleeps, we're joined by Dr. Hai Yao, a professor of oral health sciences in the College of Dental Medicine at the Medical University of South Carolina and associate department chair for the Clemson-MUSC Bioengineering Program. He also serves as the Ernest R. Norville Endowed Chair and professor of bioengineering at Clemson University. His research studies tempera mandibular joint function and disorders, also called TMJ, and why risk factors for this issue impact treatment and prevention. The TMJ makes it possible to move the lower jaw, which is important for eating and speaking.

    We are also joined by Shuchun Sun, who at the time of recording, was a senior PhD engineering student in Dr. Yao's lab, studying machine learning and biomechanics. He is currently a research associate in the Clemson-MUSC Bioengineering Program.

    Episode Links:

    Explainable deep learning and biomechanical modeling for TMJ disorder morphological risk factors

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