by
Gus Iversen, Editor in Chief | January 29, 2019
This is mainly because vendors of AI applications and technologies are now at a stage where they need to show actual business case and added value to their businesses and their customers. Medic Vision’s iQMR fits this trend – it leverages machine-learning technology for a specific goal – shortening scan time and improved efficiency.
Personally, Dr. Paul Chang, medical director of enterprise imaging at the University of Chicago, gave a presentation on the current status and challenges of AI in Radiology that I found to be very insightful and I recommend anyone in the radiology field watch or read it.
HCB News: When we think about radiology on the front lines, what do providers need today that they didn't need five years ago?
EA: To be more efficient! Time and cost efficacy are both critical drivers in today’s medical environment in general, but in the last couple of years it became greatly influential to the radiology field as well.
HCB News: What kind of news should we expect from Medic Vision looking into 2019 and the next 12 months?
EA: Demonstrating how machine-learning aided technology can be used to improve efficiencies in diagnostic imaging by new case studies and clinical studies, and expansion of our technologies to other markets beyond the U.S.
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