VIDEO: Use cases and implementation strategies for radiology artificial intelligence
Charles E. Kahn, Jr., MD, MS, Editor of the the RSNA journal Radiology: Artificial Intelligence, and professor and vice chair of radiology at the University of Pennsylvania Perelman School of Medicine. He has been heavily involved in radiology informatics and has seen up close the evolution of radiology toward deeper integration with artificial intelligence (AI).
Kahn explains there is a lot of work involved to integrate AI into radiology systems. He also said the role of AI is becoming more important as the U.S. faces a growing shortage of radiologists, and the technology can help augment radiologists to do more and improve patient care.
"Every time someone comes in and asks to install an AI application in the radiology department, it means someone has to get the legal agreements and all the contracting done, but then you have to connect it in with your systems," Kahn said.
This includes connecting it, ideally, within the EMR, PACS and other systems used by radiology. This is why several vendors go with an app store concept where a single vendor could serve as a gatekeeper to easy integration of specific AI within an existing PACS system architecture.
"For departments that want to start exploring these tools, it's an expensive proposition and takes a fair bit of resources, and not only in terms of the outright cash to purchase or license the system, but as well the IT support to build and maintain the connections," Kahn explained.
Another question radiology departments need to ask is what is the reason for adopting a particular AI algorithm. Uses cases that have been proposed for AI include a way to expand screening programs or advanced image first pass interpretations at rural hospitals and underserved and resource poor communities. A few years ago it was suggested AI may replace radiologists, but that appears to be decades in the future, if ever, Kahn said. Instead, there is an ever widening shortage of radiologists, and AI may play a role in helping augment radiologists so they can concentrate on reading cases with suspected disease or more complex cases.
Kahn also said AI may play a key role in the coming years of addressing health disparities.
"At some level, we need to find ways to where we can deliver care that is cost-effective, reaches all the people we need to reach and provided equitable healthcare, and the hope is that we can use AI to expand the reach of what we o and improve the quality of it," Kahn said.
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Dave Fornell has covered healthcare for more than 17 years, with a focus in cardiology and radiology. Fornell is a 5-time winner of a Jesse H. Neal Award, the most prestigious editorial honors in the field of specialized journalism. The wins included best technical content, best use of social media and best COVID-19 coverage. Fornell was also a three-time Neal finalist for best range of work by a single author. He produces more than 100 editorial videos each year, most of them interviews with key opinion leaders in medicine. He also writes technical articles, covers key trends, conducts video hospital site visits, and is very involved with social media. E-mail: dfornell@innovatehealthcare.com