Currently, the AI-powered prototype is being tested by a small UCLA team of hospitalists, radiation oncologists and interventional radiologists. The machine learning application, which acts like a virtual radiology assistant, enables clinicians to rapidly access valuable information while enabling them to perform other duties and to focus on patient care.
The information is delivered in multiple formats, including relevant websites, infographics, and subprograms within the application. And if the tool determines that an answer requires a human response, contact information for an actual interventional radiologist is provided. As clinicians use the application, which is focused on diagnostic and interventional radiology, it learns from each encounter and becomes smarter through deep learning techniques that provide evidence-based answers.
“The more it’s used, the smarter it gets,” says Kevin Seals, MD, resident physician in radiology at UCLA and the programmer of the application, who notes that the application’s user interface consists of text boxes arranged in a manner simulating communication via traditional SMS text messaging services.
“It feels like you’re texting with a human, but you’re texting with artificial intelligence, so the responses are coming from a computer,” observes Seals, who has a background in engineering. “For clinicians in the hospital who aren’t radiologists, it’s a way to speak with a simulated radiologist.”