AI: Reducing Radiologist Burnout and Optimizing Radiation and Contrast Dose

Radiologist burnout is an increasingly pervasive challenge in the healthcare community. A 2022 Medscape survey found that 47% of radiologists polled suffer from burnout.1 Another report published in the American Journal of Roentgenology found that between 54% and 72% of diagnostic radiologists and interventional radiologists have symptoms of burnout.2

Many clinicians and developers believe artificial intelligence (AI) technologies have the potential to significantly reduce these rates.

Reporting Tools to Reduce Burnout

As a radiologist and cofounder of Rad AI, Jeff Chang, MD, believes AI can be a “virtual assistant” that empowers radiologists to save time, improve the quality of patient care, and reduce burnout. Indeed, Dr. Chang says AI has the potential to create value for everyone from the radiology group through the ordering providers and patients.

“It’s a question of the amount of value created by the AI tool, versus the costs of implementation and support. If you have a solution that applies to as many different types of studies as possible, then that adds a lot of value across all medical imaging done by a radiology group or for a health system,” said Dr. Chang.

 

 

Considering their value and ease with which they can be integrated into an existing IT network and clinician workflows is important when choosing AI tools. “The best AI applications are as close to zero-click as possible, so it doesn’t require any change in how the radiologist works and it doesn’t require the radiologists to learn any new things,” Dr. Chang said.

That’s why Rad AI develops AI-based reporting tools that help prevent radiologist burnout by supporting a wide range of studies. “Report generation is important, because if radiologists can't do their job properly or without too much burnout, it’s going to adversely affect the patient,” said Ryan Lee, MD, MBA, MRMD, chair of radiology at Einstein Healthcare Network in Philadelphia and moderator of an AI expert forum.

 

 

Integrated Contrast and Radiation Dose Reporting

Ehsan Samei, PhD, DABR, FAAPM, FSPIE, FAIMBE, FIOMP, FACR, a professor of radiology, medical physics, biomedical computer, and electrical engineering at Duke University Medical Center, believes AI can help optimize contrast and radiation dose while generating high-quality images.

“Radiation dose measurements and quality exams are central to the very purpose of imaging. They need intentional management to characterize each in terms of metrics that matter,” said Dr. Samei.

 

 

To that end, Dr. Samei and his colleagues at Duke University developed an algorithm that created a statistical relationship between patient attributes such as height, weight, and gender, and image enhancement to optimize contrast and dose administration. “Given those parameters, we could essentially predict what would be the amount of enhancement in a patient. With that information, I can adjust the amount of contrast administration with an expected reduction in the amount delivered to the patient,” said Dr. Samei.

 

He believes the industry should use objective measures of image quality rather than subjective assessments. As such, his team uses AI to assess multiple indicators of image quality, including noise, sharpness, axial sharpness, longitudinal sharpness and contrast, to help maximize consistency and quality among sites, scanners, and readers.

“AI can help us measure the features of image quality directly from the patient’s images. We can measure image quality, which informs the consistency of care across the board. That's how we can achieve consistency and personalization of care,” he explained. He said this kind of innovation will further allow imaging to evolve beyond current standards. “I’m advocating for moving from Image Gently®3, which is based on dose reduction, to imaging well and imaging consistently.”

AI as an Ever-Present Force

As more facilities implement AI and discover its benefits, the technologies will evolve into an ever-present force, working silently in the background of every radiologist’s workflow. This will have a beneficial impact on reducing burnout and allow radiologists to accomplish more than ever before.

“As we continue to implement AI products into radiology, we will get to a point where it’s ambient AI. It’s almost invisible to the radiologist in terms of their daily workflow, but it’s involved in every single element of what they do, to streamline that workflow as much as possible,” said Dr. Chang.

References

  1. Baggett SM, Martin KL. Medscape radiologist lifestyle, happiness & burnout report 2022. Medscape. Available at: https://www.medscape.com/slideshow/2022-lifestyle-radiologist-6014784#1. Published February 18, 2022. Accessed March 24, 2022.
  2. Canon CLK, Chick JFB, DeQuesada I, Gunderman RB, Hoven N, Prosper AE. Physician burnout in radiology: perspectives from the field. Am J Roentgenol. 2022;218(2):370-374.
  3. Pediatric Radiology & Imaging | Radiation Safety – Image Gently. Imagegently.org. https://imagegently.org/. Published 2022. Accessed September 14, 2022.
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AI: Reducing Radiologist Burnout and Optimizing Radiation and Contrast Dose.  Appl Radiol. 

By McKenna Bryant| September 30, 2022
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About the Author

McKenna Bryant

McKenna Bryant

McKenna Bryant is a freelance healthcare writer based in Nashotah, WI.



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