Varun Danda, BS, and Gregg Khodorov, MD, MBA, American College of Radiology® (ACR®) Medical Student Subcommittee Artificial Intelligence (AI) and Machine Learning Co-Leads, contributed this post.

Among medical students considering a career in radiology, a common concern that comes up relates to the advancement of AI and its potential to disrupt the radiology workforce. Students are understandably weary of pursuing a field that rumors say will be replaced by machines in the near future. The issue with this sentiment is that many of these opinions come from sources that lack clinical experience in radiology and informatics. Generally speaking, “AI will replace radiologists” is a reductive talking point that could use a bit of demystifying and nuance. Radiology is still an attractive career choice, and especially in the age of AI and machine learning, it’s imperative to attract the brightest minds to the specialty to help usher in this new era of smart computational imaging. To accomplish this, the ACR Medical Student Subcommittee, in collaboration with the ACR Data Science Institute® (ACR DSI), hosted a webinar panel inviting four expert radiologists with varied backgrounds titled Beyond the Hype: How AI Will Really Impact Careers in Radiology.

The webinar kicked off with an opening address from Bibb Allen Jr., MD, FACR, ACR DSI Chief Medical Officer. Dr. Allen eloquently covered the historical perspective of once novel technologies, such as multi-detector CT and MRI, that radiologists embraced in the past in order to become more advanced, productive and accurate diagnosticians. His belief is that AI will result in an even greater generational paradigm shift in the way radiologists can deliver healthcare to their patients by increasing the amount of information obtained from medical imaging all while using less healthcare resources. He remarked that if he could restart his radiology career today to experience the impact AI will have on radiology, he would do so, without a doubt.

Paras Lakhani, MD, Associate Professor of Radiology, and one of the leaders of the AI and Deep Learning Laboratory at Thomas Jefferson University, shared his insights on the various applications of AI in radiology. Dr. Lakhani practices nuclear and cardiothoracic radiology and is an active researcher of radiology AI applications. He covered the many possible use cases for AI in radiology including interpretative assistance, image quantification, prognostic calculations as well as other less considered non-interpretative use cases including automated study protocoling, worklist prioritization, improving image quality and more.

Next, Tessa Cook, MD, PhD, CIIP, FSIIM, explored AI-related career options in radiology. As an Assistant Professor and Fellowship Director of the Radiology Informatics fellowship at the University of the Pennsylvania and incoming chair for the Society for Imaging Informatics in Medicine, Dr. Cook is a leader in radiology informatics. She discussed the spectrum of roles that future radiologists could pursue to become involved in radiology AI, from simply using AI technology in their clinical practice to researching AI and getting involved in industry to directly contribute to the creation of AI algorithms. At the end of the day, she stressed that clinical domain experience will remain essential, and radiologists will be instrumental in serving as the gatekeepers of AI to maximize its efficacy as a tool.

Finally, Lindsey Shea Johnstone, MD, MSM, provided her perspectives on the most exciting aspects of AI as a new pediatric radiology attending at Vanderbilt University Medical Center. As the most clinically junior speaker, she touched on how understanding AI and imaging informatics provided her a competitive edge in securing her first job out of fellowship. This was a sage pearl of advice for the students and trainees in attendance who may have been looking for a shorter-term reason to get involved with radiology and AI as soon as possible. She also touched on the numerous benefits that AI may have on the field of radiology in improving patient care, healthcare workflows and physician job satisfaction.

We hope that this event sparks curiosity and helps demystify the idea that radiologists will be replaced with AI. As we usher in a new era of AI and machine learning, it’s important for future radiologists to understand that they are critical in improving diagnostics, workflows and efficiency through this exciting new technological advancement. AI is to our generation what PACS was to the generation before, and what MRI/CT was to the generation before that. It benefits all of us to embrace conversations about AI in radiology and broader clinical medicine and to strive to attract students with interest in the technology to become leaders in radiology.

The ACR Medical Student Subcommittee is planning future educational webinars and resources on this important topic. Contact to stay informed.

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