VIDEO: Application of Radiomics Imaging Technology in Radiation Therapy | Artificial intelligence
ITN Contributing Editor Greg Freiherr interviews Eliot Siegel, M.D., radiology professor at the University of Maryland School of Medicine and chief of Imaging Services at the VA Maryland Health Care System.
It’s ridiculous to think that in the coming two decades, artificial intelligence will replace radiologists, says AI expert Eliot Siegel, M.D. Even if AI got good at reading medical images, “radiologists do much more than that,” he says.
In the accompanying video interview, Siegel, a radiology professor at the University of Maryland School of Medicine and chief of Imaging Services at the VA Maryland Health Care System, will highlight these and other reasons why it’s ridiculous to think computers will replace radiologists. He’ll discuss them during a SIIM debate on the subject June 2 that will include Bradley J. Erickson, M.D., associate research chair in the radiology department at Mayo Clinic in Rochester.
AI might not replace radiologists, but it could radically change the practice of radiology in just a few years, he says. During a SIIM session June 1, Siegel will moderate discussions among executives from several companies, including GE Healthcare and newcomer Aidoc, who will look at radiology AI applications and roadmap how these and future applications will incorporate AI.
One thing is for sure, says Siegel: AI is going to dramatically increase radiologists’ use of lab data, genomics and digital pathology. Several of these data types may become integral parts of reading oncologic images, according to Siegel, who will provide details at SIIM May 31 in “Point-of-Care Precision Medicine: Real-time Radiomics-Genomics in the Reading Room.”
Editor’s note: This pre-SIIM video interview is the first in a series of three by Greg Freiherr. The series features industry luminaries discussing key issues associated with the upcoming SIIM conference. The first interview, Building An Effective Enterprise Imaging Strategy featuring Kim Garriott, can be viewed here.
Related Video: ITN Editorial Director Melinda Taschetta-Millane discusses “Machine Learning and the Future of Radiology” with Eliot Siegel at SIIM 2017.