With the annual Radiological Society of North America meeting just weeks away, HealthCare Business News checked in with RSNA president, Dr. Curtis P. Langlotz, to find out more about his career, and his background with the organization.
We also talked about what attendees can expect at the upcoming meeting at McCormick Place in Chicago.
HCB News: Who or what inspired you to pursue a career in radiology? Dr. Curtis P. Langlotz: During my radiology clerkship, I was the medical student in the back of the room with his hand up who could see the finding and was urgently wanting to be called upon. I liked the fact that radiology often had the answer to the diagnostic mysteries in the hospital. And I enjoyed the challenge of integrating information from the images into a coherent patient story—like solving a crossword puzzle. Finally, although it wasn’t as clear when I started my training, radiology turns out to be a high-tech digital specialty, which has presented many interesting research questions for a radiologist trained as an informatics/AI researcher.
HCB News: As a leader of the society, what have been some of your top initiatives or priorities? CL: I am particularly proud of the RadioGraphics article in which the RSNA board expressed regret for the organization’s past discrimination and laid out a set of concrete steps that the organization is taking to address those issues. RSNA is committed to initiatives and policies that create a more equitable and inclusive profession and society.
Much of my time on the board has been focused on AI. We have developed several programs and resources that give RSNA members the tools they need to integrate AI systems into clinical practice. For example, the RSNA Imaging AI Certificate program is the first AI education program developed specifically for radiology. Just as we need to understand how an MRI machine works so we aren’t fooled by a phase artifact, we need to understand how AI works so we can implement and use it safely. One of the great things about the online course is that it blends a case-based curriculum with practical applications to help radiologists understand how to leverage AI for their practice.
I am also proud of our annual AI challenges, which are a great way to engage the data science community and to aggregate high quality AI-ready datasets that can be used to train AI algorithms. The most recent challenge—on characterizing degenerative disease of the lumbar spine—attracted more than 1,800 teams.
