Four AI Workflow Trends from RSNA 2019

By December 19, 2019Events, Blog

The Biggest Trend: Maturing Implementation of AI

Attendees who visited our booth last year were interested in learning about AI capabilities. This year they were bringing questions about implementing infrastructure needed for AI and how to scale AI research in their organizations. Scaling access to clinical data and interoperability appears to be a rising concern this year. Organizations are also gradually accepting cloud scaling as a secure option.

Radiologists are beginning to plan for AI in their standard workflows. There were many radiologists in our booth asking questions with respect to AI research in their current clinical workflows.

Data Curation for Research Still Falls Short

The focus in many workshops and presentations from radiologists was “data wrangling” and data set quality. We received many questions from attendees regarding metadata management and labelling tools. At the same time there is growing recognition that clinical systems don’t meet the needs of the research and AI development communities. Additionally, an entirely new class of solution that supports the research workflow is needed.

We recommend Dr. Paul Chang’s (University of Chicago) AuntMinnie interview during RSNA: “AI is like a great car … Most cars still need gas and roads. In the context of this analogy, gas is vetted data and the road is workflow orchestration that is AI-enabled… The only way to make a transformative technology real is to do the boring stuff, the infrastructure stuff.”

Everyone Noticed the Busy AI Showcase

The AI Showcase was very active this year. In 2018, there were roughly 70 vendors in the AI Showcase, but this year there were 129, including many international AI vendors. We noticed growth in AI development for cardiac and brain imaging.

It’s Imminent: Equipment Vendors are Integrating AI Workflows

AI is moving beyond the desktop as imaging equipment manufacturers have their eye on supporting research workflows. Leading equipment manufacturers like Philips and Canon displayed developments in their interfaces to support AI or analysis tools in a disease specific applications. Flywheel is expanding partnerships with AI vendors and equipment vendors in addition to supporting clients performing imaging and clinical research.

CEO Travis Richardson presenting at the Google Cloud Booth about Flywheel’s scalable infrastructure for machine learning.

Esther Shmagin

Esther Shmagin

Marketing Specialist at Flywheel