What’s Next in Medical Imaging with Cloud and AI Technologies?

Enhancing Medical Imaging with Cloud Technology

Nuance chief strategy officer Peter Durlak points to the advantages of cloud technology in an AI-aided medical imaging program: Graphics processing unit-intensive processes can be scaled up and down again without the huge expense of on-premises infrastructure.

“You have this burst capability and that predictability in the cloud, and IT staff don’t have to worry about deploying these constantly changing systems inside the hospital that they could never keep up with. Or couldn’t afford,” he says.

The cloud also allows for rapid experimentation and implementation, Illing says, pointing to the example of Adoc’s always-on, AI-based decision support software, which analyzes CT scans to flag abnormalities and diagnose life-threatening cases. does to give priority to

“They want to regularly release new algorithms targeting new pathologies, and the cloud provides the development velocity needed for this,” he says.

As AI develops and machine learning models become more advanced, Durlach explains, medical imaging’s potential to improve early detection capabilities for cancer and other diseases is cause for hope in health care.

“If you can do AI for cancer screening on a large scale, these models can be used to help select patients who are most likely to have something suspicious, the impact on care would be astronomical,” he said. They say. “Finding patients you might have missed or early detection of disease is probably the greatest clinical value that’s going to come from this.”

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