AI and radiology: fear, hype, and hope

I believe automation with the aid of AI can help us reduce errors and more easily perform routine tasks so we can better focus on making a diagnosis. We can take a greater role in relating with patients and spend more time explaining, counseling, teaching, and making discoveries.
By

There is one topic that is top of mind for radiologists right now: how artificial intelligence (AI) will transform our profession.

There is a great deal of optimism and excitement about how these innovations will improve radiology in the coming years. But in some circles, I also hear concerns that technology will alter the profession and disenfranchise those who practice it.

I thought it was important to address both perspectives and share my own, at the Radiological Society of North America’s annual conference in Chicago last week. I called my talk “AI’s role in radiology: fear, hype and hope” because there is a mixture of all these emotions among my peers.

Some recent articles have predicted a significantly diminished role for the radiologist in an AI-powered healthcare environment. But I don’t believe this is our future.

While people have made predictions about technology replacing humans for decades, humans have an incredible ability to create new sources of value and to leverage technology instead of being replaced by it. For example, while a lot of people use tax preparation software, it has not replaced tax preparers. By using software, the tax preparers are providing better services to the taxpayers. While ATMs may perform a lot of the job of bank tellers, there are actually more bank tellers now than before the advent of ATMs.

It is true that technologies can eliminate certain jobs, but they can also increase demand for services and products. For example, while desktop publishing may have taken work from typographers, there are many more graphic designers today.

When it comes to the question of will AI replace radiologists, Dr. Curtis Langlotz, a radiology professor at Stanford University and one of my mentors in imaging informatics, put it best when he said, “The answer is no. But rads who use AI will replace rads who don’t.”

We can’t predict what the job of the radiologist will look like 5, 10, or 20 years from now. But my hope is that AI will improve every aspect of the imaging value chain, including patient scheduling, imaging protocols, workflow, creating actionable reports, communication, quality assessment, and patient safety and follow-ups.

I believe automation with the aid of radiology AI  can help us reduce errors and more easily perform routine tasks so we can better focus on making a diagnosis. We can take a greater role in relating with patients and spend more time explaining, counseling, teaching, and making discoveries.

Throughout history, radiologists have been able to adopt new technological innovations and integrate them into daily workflow. I hope we can stop worrying about AI replacing radiologists and instead start collaborating on innovative solutions that maximize positive potential for the field and the patients we serve.

AI’s Role in Radiology: Fear, Hype, and Hope (PPTX) from NuanceHealth

 

Learn more by connecting with us on LinkedIn and Twitter 

Tags: ,

Woojin Kim

About Woojin Kim

Dr. Kim is Chief Medical Information Officer at the Healthcare Division of Nuance Communications. He was a co-founder, member of the Board of Directors, and Director of Innovation at the Montage Healthcare Solutions, which was acquired by Nuance in 2016. In the past, Dr. Kim had served as interim Chief of Division of Musculoskeletal Imaging, Director of Center for Translational Imaging Informatics, and Chief of Radiography at the Hospital of the University of Pennsylvania. He completed his radiology residency and MSK fellowship training at the Hospital of the University of Pennsylvania. Also, he completed an Imaging Informatics fellowship at the University of Maryland/Baltimore VA Medical Center. Dr. Kim has been an active member in imaging informatics within various societies, including ACR, SIIM, and RSNA, with focus on data mining, analytics, and machine learning.