Artificial Intelligence with a Human Touch

Despite the remarkable progress in artificial intelligence (AI), several studies show that AI systems do not improve radiologists' diagnostic performance.

In fact, diagnostic errors contribute to 40,000 - 80,000 deaths annually in U.S. hospitals.

This lapse creates a pressing need: Build next-generation computer-aided diagnosis algorithms that are more interactive to fully realize the benefits of AI in improving medical diagnosis.

That’s just what Hien Van Nguyen, University of Houston associate professor of electrical and computer engineering,

 is doing with a new $933,812 grant from the National Cancer Institute. He will focus on lung cancer diagnostics.

“Current AI systems focus on improving stand-alone performances while neglecting team interaction with radiologists,”

 said Van Nguyen. “This project aims to develop a computational framework for AI to collaborate with human radiologists on medical diagnosis tasks.”

That framework uses a unique combination of eye-gaze tracking,

intention reverse engineering and reinforcement learning to decide when and how an AI system should interact with radiologists.


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