The hardest part of healthcare imaging AI is rarely the model. By the time a solution reaches your hospital it has usually cleared validation and shown strong results on benchmark data. Then it enters the reading room and stalls. The failure is almost never algorithmic. It is operational.
The model is the easy part
Vendors compete on accuracy, and most proven imaging AI performs well in controlled studies. But a model that flags a finding in a research notebook is not the same as one that surfaces that finding inside a radiologist's worklist, at the right moment, without extra clicks. The gap between those two states is where projects quietly die.
Where projects actually break
Across stalled deployments, the same three failure modes recur, and none of them are about the algorithm.
1. It never connects
Promising AI stays isolated from PACS, RIS, and EHR. Without DICOM routing and HL7 or FHIR messaging, output lives in a separate portal nobody opens. If the insight is not in the diagnostic flow, it is not used, no matter how good it is.
2. It adds friction
If AI does not fit how radiologists already read, it gets bypassed. Every extra login, tab, or manual step erodes adoption until the tool is abandoned. Accuracy cannot rescue a workflow people route around.
3. Nobody owns the last mile
Vendors sell the model. IT is stretched thin. The space between "purchased" and "working" (integration, workflow design, training, monitoring) has no clear owner. That gap is exactly where ROI disappears.
Implementation is the other 80%
Treating deployment as a one-time install underestimates the work. A program that delivers manages the full lifecycle: pre-sales alignment, environment-specific deployment, PACS/RIS/EHR integration, and ongoing optimization against real clinical outcomes.
If you are evaluating imaging AI, weigh vendors less on benchmark accuracy and more on the path to a working, integrated, adopted system. The model is necessary. It is not sufficient.