From missiles to cancer cells

From missiles to cancer cells

par Sean Ruck, Contributing Editor | August 09, 2019
From the August 2019 issue of HealthCare Business News magazine


The algorithm Veidman and his team uses reviews the image in the highest resolution available. In the case of the pathologist, the AI could help to not only detect the areas they might have missed, but make the work more efficient.

While there is a lot of promise for AI to tackle the data, there is still work to do in order to close the information gap. “I’ll give an example with a scanning algorithm in the case of prostate cancer. When someone detects what might be prostate cancer, another pathologist will review that case,” Veidman says. “In 30 to 40 percent of instances, the cases are benign and no one else is going and reviewing those benign cases. So we have an algorithm that reviews all the cases and helps to determine if there were any discrepancies or a misdiagnosis, and then it raises the flag if necessary to let the pathologist know to review those particular images again.”

THE (LEADER) IN MEDICAL IMAGING TECHNOLOGY SINCE 1982. SALES-SERVICE-REPAIR

Special-Pricing Available on Medical Displays, Patient Monitors, Recorders, Printers, Media, Ultrasound Machines, and Cameras.This includes Top Brands such as SONY, BARCO, NDS, NEC, LG, EDAN, EIZO, ELO, FSN, PANASONIC, MITSUBISHI, OLYMPUS, & WIDE.


The more information fed into AI, the more powerful the tool. Veidman acknowledges this, and that was why he worked to develop partnerships with the biggest hospitals in Israel. Those relationships provide a pool of 20 million slides to access — critical for developing the algorithms that deliver the accuracy and specificity needed.

Veidman says there are pathologists reviewing those slides and marking them — benign, malignant, etc. — in order to train the AI. For those in the field doing imaging of patients, the technology largely runs in the background, with little impact on their work.

While he’s optimistic on the convergence of AI, satellite imaging theory and medical imaging, he’s less confident on when we might see a high level of adoption. “It’s a prediction that involves human beings, so it’s probably the hardest. We could take a look at the past to get some examples though. What was the time it took all the systems to get new technology involving IT in the past? Like EHR for example? Maybe it was 10 or 15 years, maybe still ongoing in some places,” he says. “I think it will continue to grow steadily, especially in radiology and ophthalmology and very soon in pathology. It’s going to be a very long journey because of the technology, people reluctant to make the change to something new, and of course, regulations. But, the train is out of the station.”

Back to HCB News

You Must Be Logged In To Post A Comment