Current landscape and future trajectory for AI in healthcare

por Lauren Dubinsky, Senior Reporter | February 04, 2019
Artificial Intelligence Health IT
From the January/February 2019 issue of HealthCare Business News magazine


“If we are going to put our trust in these heavily automated augmented systems, the human operators of these systems have to be able to build intuition about what the system is doing and why it’s taking certain actions, and so on,” said Singh.

Many AI systems are worked on in the research setting, but not all translate into a product. If they are stuck in the academic setting, they are simply explorations of hypotheses instead of actual AI systems.

stats
DOTmed text ad

Reveal Mobi Pro now available for sale in the US

Reveal Mobi Pro integrates the Reveal 35C detector with SpectralDR technology into a modern mobile X-ray solution. Mobi Pro allows for simultaneous acquisition of conventional & dual-energy images with a single exposure. Contact us for a demo at no cost.

stats

Lastly, the system has to be able to learn as the data evolves.

“For example, if the system spots that a new patient risk segment has emerged or a new type of payment fraud has begun to exist, then it should be able to update the human operator that something has changed, and [suggest] an action [they] might take in fixing that,” said Singh.

AI and radiology
There is no shortage of AI systems and applications for the radiology field. In the past year, more than a handful of new products came to market.

One of the most recent is Philips Healthcare’s Illumeo PACS with adaptive intelligence, which was a major focus at this year’s Radiological Society of North America (RSNA) annual meeting. The University of Utah Health recently became one of the first to leverage this technology.

The health system is tasked to review about 500,000 cases per year due to its large referral base. Before Illumeo was installed, the radiologists had to pull up a case in the PACS and search for the matching new and old images.

Illumeo leverages adaptive intelligence and analytics to automatically find those matching images. It then positions them side-by-side so that the radiologist can determine if the lesions got bigger over time.

Dr. Richard Wiggins
“One of the biggest things is the amount of data,” said Dr. Richard Wiggins, a neuroradiologist at the health system. “We all know that we have to look at more and more data all the time for our studies and [some may be] getting reimbursed even less now than they were years ago for those studies.”

When he started his career, he was looking at between 2,000 and 2,500 images per day, but now he looks at about 250,000 per day. With the help of Illumeo, Wiggins and his colleagues are saving a lot of time.

MaxQ Artificial Intelligence also showed off its new AI technology at this year’s RSNA annual meeting. Its Accipio Ix application automatically flags non-contrast head CT scans with suspected intracranial hemorrhages so that those cases go to the top of the radiologist’s list.

You Must Be Logged In To Post A Comment