How University of Rochester Uses AI to Reduce Risk of Failed Follow-Up

“Every patient deserves their best chance for a cure,” says Ben Wandtke, an associate professor of imagining sciences. “If there’s a technology out there that helps make this a reality, you absolutely should embrace it. The problem of delayed diagnosis related to actionable radiology findings is universal, but it does not have to be. Natural language processing-based analytics coupled with database technology and a human touch have nearly eliminated this problem at the University of Rochester.”