by
Sean Ruck, Contributing Editor | June 07, 2019
From the June 2019 issue of HealthCare Business News magazine
With both the data and the people who would benefit from the data all contained within a hospital, it stands to reason that algorithms should be made within the hospital. “The problem with that is there are no data scientists inside the hospital,” Dreyer says.
Enter the American College of Radiology’s Data Science Institute. The DSI’s role is to promote the deployment of safe AI into facilities for medical imaging to better care for patients. The institute is piloting a project with the FDA’s MDDT program to certify algorithms, and later it assesses them in the marketplace after deployment.
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The ACR has a program called Assess-AI that also monitors algorithm performance in order to satisfy FDA requirements and provide information to developers in order to improve and enhance algorithms.
For facilities (nearly all) that don’t have data scientists, Dreyer says the ACR launched AI-LAB in May. AI-LAB offers a vendor-neutral solution that goes into hospitals for free to create AI models. Dreyer believes this is a step in the right direction. He thinks everyone should be able to create who wants to. “The tagline we use for this is ‘democratization of artificial intelligence.’ We also hope vendors will provide robust, purchasable solutions for AI creation; if you want a Ferrari instead of a VW, you should be able to get to that if you pay,” he says. “If you want a car, though, everyone should be able to get it.”
To improve and evolve AI further, public relations efforts may need to be brought to bear. “AI algorithms need large amounts of data. “Fortunately, there are technical solutions such as transfer learning, federated learning and ensembles that will help provide the power of combined data without the need to move it off premises to a single location.” he says.
It’s still a long road. Most systems today still require human decision. The ultimate arbiter of truth is human. While Dreyer says you could create a system more accurate than humans, it’s still up to the diagnostician what to say in the report. For a radiologist, there are going to be times the equipment and algorithms are so sensitive that things may me found that can be disregarded with reason. Understanding the broad picture and making judgment calls like that is something AI is probably years from managing.
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