In a recent article published in Radiology Today, Bibb Allen, MD, FACR, CMO of the ACR's Data Science Institute (DSI), notes, "AI-lite" is already being used in radiology in a number of ways, such as computer-aided detection for cancer, auto-segmentation of organs in 3D postprocessing, natural language processing to facilitate critical results reporting, consultation of best guidelines for recommendations, and quantification and kinetics in postprocessing.
In the article, Allen goes on to say "We believe that AI is poised to significantly increase the value radiology professionals are able to provide their patients," Allen says. "Adding information acquired from AI algorithms to our reporting and workflow can significantly improve patient care. While AI for imaging will not come all at once, early adopters of AI in their practices will be ready to be future leaders in health care."
The publication also quotes Bradley J. Erickson, MD, PhD, an associate professor of biochemistry and molecular biology at the Mayo Clinic College of Medicine in Rochester, Minnesota, who notes that "AI is a broad field of many technologies for training computers to behave like intelligent beings. Machine learning—algorithms that focus on recognizing data patterns—is based on training data sets that include several examples and an answer—for example, images labeled 'cancer' or 'not cancer.'"
Deep learning, a subfield of machine learning that relies on artificial neural networks, is of particular interest in radiology. "The power of deep learning is that, given a large enough training set, the computer can learn the most useful features for the task by itself," Erickson says.
At RAD Sherpa, we're committed to bringing these AI advances to radiologists worldwide, helping them deliver faster, more accurate diagnoses while managing increasing workloads.