New disruption comes to radiology
Supercomputers bring advantages and challenges to radiologists
New disruption comes to radiology
Twenty years ago, picture archiving and communication systems came into healthcare and changed radiology. Film was used for display, storage and transfer, recalls Keith Dreyer, D. O., vice chair of radiology at Massachusetts General Hospital and an associate professor of radiology at Harvard Medical School.
With PACS came display monitors, storage off-site and distribution changes such as moving medical images to the Internet. The promise of PACS was that it would increase radiologists’ productivity—it takes a while to hang up 50 images of one patient and look through all the images, Dreyer notes. With PACS, productivity increased and accuracy improved, as radiologists were seeing things they never saw before on film.
But that doesn’t mean all radiologists were quick advocates. PACS meant a loss of control—those images could easily be transmitted elsewhere and if images left a department too soon before being interpreted and landed in surgeons’ hands, the fear among radiologists was that “we would lose our value,” Dreyer says. “If a surgeon got the images before our interpretation, why wait for the interpretation?”
Now, that fear and disruption may come back to some degree with the emergence of learning machines, such as the Watson supercomputer, and Dreyer, along with Richard Baron, M.D., a radiologist and professor of radiology at the University of Chicago Medical Center, will seek to calm nerves during an educational session at RSNA 2016.
Artificial intelligence will be disruptive, that’s not a negative just the reality, they will tell colleagues. But much will be beneficial disruption. Just like a pilot should not be flying a plane alone without a certain amount of experience, artificial intelligence may not be appropriate for newer radiologists until they get more experience. But artificial intelligence and the automation it brings to those best able to use it will bring benefits to clinicians and their patients, Dreyer contends. “You want to make sure you have a seasoned pilot on a flight.”
Part of the session also will cover radiologists’ bedside manner, with Baron telling colleagues that they have to do more than interpretations; they must learn how to better interact with patients. Because PACS were more efficient, the volume of imaging studies exploded and radiologists did not have the opportunity to spend more time with patients, Dreyer says. But that’s got to change now as artificial intelligence comes in. “We have to do this with the more advanced systems.”
The session, “When Machines Think: Radiology’s Next Frontier,” is scheduled at 8:30 a.m. on Sunday, Dec. 4, in the Arie Crown Theater.
Twenty years ago, picture archiving and communication systems came into healthcare and changed radiology. Film was used for display, storage and transfer, recalls Keith Dreyer, D. O., vice chair of radiology at Massachusetts General Hospital and an associate professor of radiology at Harvard Medical School.
With PACS came display monitors, storage off-site and distribution changes such as moving medical images to the Internet. The promise of PACS was that it would increase radiologists’ productivity—it takes a while to hang up 50 images of one patient and look through all the images, Dreyer notes. With PACS, productivity increased and accuracy improved, as radiologists were seeing things they never saw before on film.
But that doesn’t mean all radiologists were quick advocates. PACS meant a loss of control—those images could easily be transmitted elsewhere and if images left a department too soon before being interpreted and landed in surgeons’ hands, the fear among radiologists was that “we would lose our value,” Dreyer says. “If a surgeon got the images before our interpretation, why wait for the interpretation?”
Now, that fear and disruption may come back to some degree with the emergence of learning machines, such as the Watson supercomputer, and Dreyer, along with Richard Baron, M.D., a radiologist and professor of radiology at the University of Chicago Medical Center, will seek to calm nerves during an educational session at RSNA 2016.
Artificial intelligence will be disruptive, that’s not a negative just the reality, they will tell colleagues. But much will be beneficial disruption. Just like a pilot should not be flying a plane alone without a certain amount of experience, artificial intelligence may not be appropriate for newer radiologists until they get more experience. But artificial intelligence and the automation it brings to those best able to use it will bring benefits to clinicians and their patients, Dreyer contends. “You want to make sure you have a seasoned pilot on a flight.”
Part of the session also will cover radiologists’ bedside manner, with Baron telling colleagues that they have to do more than interpretations; they must learn how to better interact with patients. Because PACS were more efficient, the volume of imaging studies exploded and radiologists did not have the opportunity to spend more time with patients, Dreyer says. But that’s got to change now as artificial intelligence comes in. “We have to do this with the more advanced systems.”
The session, “When Machines Think: Radiology’s Next Frontier,” is scheduled at 8:30 a.m. on Sunday, Dec. 4, in the Arie Crown Theater.
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