Supplemental AI education during residency linked to key benefits
Radiology residents who complete supplemental education on artificial intelligence solutions nearly unanimously perceive such programs as beneficial, but there is still no standardized AI curriculum for emerging radiologists.
In 2019, the Radiological Society of North American rolled out its Imaging AI Foundational Certificate program to help radiology professionals of all experience levels become more familiar with the emerging technology. Recently, two radiology residency programs under the Mount Sinai Health System umbrella saw firsthand how beneficial the added AI curriculum is for residents.
Residents who completed the four-month course showed a nearly 40% improvement in their AI knowledge and skills assessments scores.
Although the rapid evolvement of AI technology and software can make developing appropriate educational materials on the topic feel like hitting a moving target, authors of a new analysis in Academic Radiology indicate that these efforts of organizations like RSNA are paving the way for structured AI education. [1]
“Given the growing breadth and depth of AI, developing a set curriculum for radiology trainees can be viewed as a particular challenge,” corresponding author Mark Finkelstein, MD, with the Icahn School of Medicine at Mount Sinai in New York, and colleagues explain. “Learning to effectively utilize AI requires more than lecture materials, but also hands-on practice to build and support real-world skills.”
RSNA’s AI certification program consists of six online modules containing didactic lectures and post-module assessments. Before starting the course, the Mount Sinai residents who participated in the program completed an initial assessment to measure their understanding of AI in radiology; the same assessment was distributed after course completion.
The mean pre-course assessment score was 37%. After course completion, the residents recorded significant improvements in their AI knowledge and skills, raising their scores to an average of 73%. These improvements were seen across the board, regardless of resident year and self-reported prior understanding of AI.
The majority of residents (74%) also reported feeling like they had greater familiarity with the role of AI in radiology after taking the course, and more than half (57%) expressed interest in taking additional courses to improve their understanding of AI—something the authors perceive as a positive sign for the future.
“This is a promising finding and paves the way for the development of more advanced AI training modules or continuing education courses in the future,” the authors write, adding that such training should build on foundational training and familiarize participants with the AI lifecycle, from AI training and test data curation to clinical deployment.
Learn more about the residency programs’ experience with RSNA’s AI certification here.
In addition to her background in journalism, Hannah also has patient-facing experience in clinical settings, having spent more than 12 years working as a registered rad tech. She began covering the medical imaging industry for Innovate Healthcare in 2021.