Radiology reports often end with vague language like “Please correlate clinically” or “Further imaging may be warranted.” This defensive phrasing does not indicate urgency or specific next steps, leaving ordering providers unsure whether to act now or later. It is one symptom of a larger structural problem in imaging. While artificial intelligence in radiology has made significant gains in detection, most industry conversation focuses on the front end of the workflow—what the model can find—while largely ignoring the back end: what happens after the finding hits the report.
Every flagged finding is the beginning of a workflow. A follow-up study needs to be ordered. A patient needs to be contacted. A referral may need to be placed. A timeline needs to be tracked. When the finding is serious, those steps carry genuine clinical urgency. When they do not happen, patients get lost. The data on this is sobering. Research published in the Journal of the American College of Radiology found that overall adherence to recommendations for additional imaging of incidental findings was just 39.1%. Other studies put the figure closer to 50 percent. However you measure it, the gap between what is found and what gets followed up on is enormous, and it widens as imaging volume grows.
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And volume is growing. The Neiman Health Policy Institute projects that imaging utilization could increase by as much as 26.9% by 2055, while radiologist supply is expected to grow at a roughly comparable rate, meaning the current shortage is unlikely to improve without deliberate intervention. Radiologist attrition has accelerated since the pandemic, with departure rates up 50% from pre-COVID levels. Under that kind of pressure, report language gets less specific, recommendations get more vague and the downstream infrastructure, which was never adequate to begin with, absorbs more volume than it can handle.
Health systems have tried to address the follow-up gap with worklists, tracking spreadsheets, and manual processes. In many organizations, that manual reconciliation is not a temporary workaround. It is the process. It is also the reason people fall through the cracks. The limitation of most existing approaches is that they create visibility without creating accountability. A dashboard can tell you that a lung nodule was flagged. It cannot often tell you whether the follow-up was ordered, whether the patient was contacted, whether the appointment was scheduled, or whether the result came back. Those are different operational problems, and each one requires a different handoff.
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What is needed is infrastructure that connects detection to completed care. Not just a view of what was found, but an operational layer that routes findings based on actual clinical risk, manages outreach, tracks completion, and escalates when something stalls. Without this, the industry risks solving the wrong problem. Better detection tools surface more findings. More findings generate more downstream work. And the hard task of translating a finding into actual care relies on a workforce and systems already running at capacity. This structural mismatch creates a cycle where technological advancement does not necessarily translate into improved patient outcomes.
