From the June 2019 issue of HealthCare Business News magazine


Erickson believes that once AI starts to be used more, the industry will realize that it’s not as easy as simply connecting something to their PACS.

“I think many of the small companies that have developed AI-type products have not thought about the enterprise aspects in terms of how to integrate with the rest of the informatics infrastructure that is present in a hospital, how to get their results back and how to make sure that it works 100 percent of the time and not just enough for a demonstration,” said Erickson. “I think that next level of maturity hasn't really happened yet.”


Another challenge with AI, according to Primo, is that many of the results are currently anecdotal, and outcomes can be highly dependent on factors such as the image acquisition process, signal-to-noise ratio, examination techniques and the make and model of the imaging modality.

“The reproducibility of AI outcomes will be essential to overcome the conundrum, the plural of anecdote is not data,” said Primo, citing a popular phrase that cautions against misleading evidence. “For AI-driven image interpretation to become widely accepted in the radiology community, standardization needs to be addressed.”

Image acquisition protocols influence how effective an AI algorithm can be. For instance, an AI algorithm can be trained to work well with a specific CT or MR scanner, but if that algorithm is used with a different manufacturer’s CT or MR the results may be different.

“A first step will probably be exploring standardized imaging acquisition processes, image quality and other variables to be used with AI applications,” said Primo. “Only then, could AI applications become more vendor-neutral.”

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