Why AI Radiology Still Struggles at the Last Mile
A new look at radiology AI argues that detection is improving faster than clinical follow-through. The real bottleneck is not whether software can find abnormalities, but whether systems can ensure those findings lead to timely action.
Radiology AI has made impressive gains at the point of detection, but detection is only the first step in a much longer chain of care. If a model identifies a suspicious finding and that result never gets acted upon, the clinical value collapses. That gap — the last mile between algorithm output and patient outcome — is where many promising tools underperform.
This is an important corrective to the industry’s usual emphasis on sensitivity and specificity. Those metrics matter, but they are not the same as real-world impact. Workflow handoffs, alert fatigue, follow-up scheduling, communication with ordering clinicians, and patient navigation all determine whether an image finding changes a diagnosis in time.
The article highlights a core problem in healthcare AI: we tend to validate what is easiest to measure, not what matters most. Vendors can demonstrate that a model sees something; health systems need proof that the finding triggers the right pathway at the right time. That requires operational redesign, not just software deployment.
The strongest AI products in radiology may therefore be the ones that behave less like standalone detection engines and more like coordination infrastructure. In other words, the market may increasingly reward tools that close workflow loops, not just tools that raise flags.