This approach keeps control with radiologists. Each site can set its own accuracy thresholds, retrain as imaging protocols evolve, and automatically generate normal study reports that no longer require manual dictation. Instead of writing the same paragraph hundreds of times, radiologists can review, edit, and sign off in seconds.
Think of it as adding a virtual resident: one that pre-drafts reports, organizes patient information, and flags studies that need urgent attention all within the systems clinicians already use. With modern infrastructure, this kind of tuning and deployment can happen more rapidly than traditional approaches.
The cultural shift: Education and control
Most new radiologists aren’t learning enough about AI because most programs lack the resources or faculty to teach it. The RSNA-SIIM National Imaging Informatics Curriculum has helped fill some of that gap, but it only reaches a fraction of residents each year. There’s no standardized requirement, no consistent exposure. Right now, AI education in radiology is still more self-taught than structured.
That has to change. As imaging volumes rise and staffing lags, understanding how to work with AI will be as essential as learning how to read a chest X-ray. The next generation of radiologists will need to know how to review model performance, fine-tune for local protocols, and edit pre-drafted reports efficiently. These are the skills that, when combined with technology, will be what keeps the profession sustainable.
Embedding AI and informatics into residency training will give radiologists the tools to manage growing workloads safely and intelligently. The sooner we make this part of standard training, the sooner we can close the gap between demand and capacity.
The tipping point
The staff shortage is real and at a crisis point. Talented radiologists are struggling, and there’s no way to out-train or out-hire our way out of it.
Radiology is at a key inflection point, and it must decide: either adopt AI that works for radiologists, or watch others adopt it without them. Nurse practitioners and physician assistants will eventually bypass radiology and leverage AI if radiologists won’t.
The good news is that the technology is ready. Platform-based AI can now be tuned to each site’s data, deployed within existing systems, and scaled safely across networks. This is practical infrastructure that can be implemented and make a meaningful impact.
If radiology embraces this technology now, we can give radiologists the tools they need to succeed.
