Radiology at a breaking point: How platform-style AI can triage demand and streamline reporting

By Khan Siddiqui

From the outside, my wife’s radiology job looks ideal: short commute, great institution, some flexibility to work from home. The reality? She starts at 6:30 a.m., gets home for dinner, then heads back to the reading room until midnight. Her experience is unfortunately the reality of the profession for thousands in the field right now.

Radiologists like my wife are suffering from severe burnout due to staffing shortages.

Each year, roughly 1,100 new radiology residents enter training. This is a number that has barely budged in a decade, even as imaging volume grows about 7% annually. Demand keeps climbing, but the training pipeline hasn’t expanded to match it. The result is delayed reads, mounting backlogs, and longer turnaround times for essential scans. At some health systems, chest X-rays can take six days or more to be read.

The shortage is compounded by an aging population, longer lifespans, and changing practice patterns, all of which contribute to increased demand for imaging. Workflows across emergency departments and primary care depend on imaging, and many new therapeutics require serial scans for safety monitoring. Radiologists entering the workforce have not been trained to use AI tools that could significantly lighten their load.

We can fix this. In fact, radiology is sitting on one of the biggest opportunities in healthcare: use AI to return valuable time to clinicians and deliver faster results.

The problem isn’t identifying disease, it’s writing about it.
Radiologists are extraordinarily fast at perception. Spotting a 1-cm lung nodule can take a quarter of a second, the same amount of time it takes you to recognize a familiar face. The real time sink is converting what we see into a clean, standardized report. That cognitive load, when done hundreds of times a day, is the burnout engine.

Demand keeps rising due to an aging population, imaging-reliant workflows across ED and primary care, and new therapeutics that require serial scans, compounding demand. We won’t hire or train our way out of this. The only scalable lever is efficiency, measured in minutes saved per study and days shaved off turnaround times.
That sounds like a crisis. I see an opportunity: if we cut documentation time from minutes to seconds and auto-handle normal studies, we change the math and give radiologists back the time and focus that burnout has taken away.

The fix: Platform-based, site-tunable AI
Radiology doesn’t need another point solution; it needs a new foundation. The path forward is a platform-based model that radiology teams can train and tune to their own data without starting from scratch. It connects directly into existing PACS or RIS systems, fine-tunes on local cases, and continuously learns from real-world performance.

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.

Platform-style AI can help radiologists meet rising demand without sacrificing quality, giving them back the time, focus, and energy that burnout has taken.

With the right technology, radiologists can focus on what matters: accuracy, efficiency, and patient care.