If you think new imaging IT technologies will reduce radiologist workload in the future, you might want to think again. Researchers who analyzed hundreds of studies on new scientific advances predicted that nearly half of them would increase radiologists’ workload – especially AI.
Radiologists are desperately in need of help to manage rising imaging volumes during a period of global workforce shortages.
- New imaging IT technologies are often held up as possible solutions that could help imaging personnel do more with less.
But how true is that belief? In the new study in European Journal of Radiology, radiologists Thomas Kwee, MD, and Robert Kwee, MD, from the Netherlands analyzed a random sample of 416 articles published in 2024 on imaging applications that could affect future radiologist workloads, finding …
- 49% of the articles on applications that had the potential to directly impact patient care would increase radiologist workload in the tertiary care academic setting.
- Studies on AI-focused applications were 14X more likely to increase workload compared to research that didn’t.
- Similar numbers were found for non-academic general teaching hospitals.
- The findings are largely similar to a 2019 study by Kwee et al that used the same methodology.
Why don’t new imaging applications show more potential to reduce radiologists’ workloads?
- The Kwees found that image post-processing and interpretation times have grown for both existing and new applications.
In the specific case of AI, they cited an example in which a deep learning algorithm was introduced to analyze CT scans to segment and classify features of spontaneous intracerebral hemorrhage and predict hematoma expansion.
- The model successfully predicted hematoma expansion and automatically segmented lesions, but CT images still had to be post-processed with a separate workflow. This required additional radiologist interpretation time and extended their workload.
The Takeaway
The new study throws cold water on the idea that AI will be able to solve radiology’s workload dilemma. It’s possible that AI will have an impact on radiology that’s similar to that of PACS in the 1990s in making radiologists more productive, but we’ll need new efficiency-oriented changes to achieve that goal.
