Why imaging AI projects stall after the demo

A demo shows a model on prepared images. Daily use is different. Here are patterns that commonly slow projects down.

The result lives somewhere else

If radiologists must open a separate application, many will not. Returning the result into the PACS study removes that step.

Validation is skipped or vague

Agreeing before go-live on what "good enough" means, tested on your own studies, avoids arguments later.

Nobody owns the connection

Routing rules, certificates and software updates change over time. Without an owner and monitoring, silent failures can go unnoticed.

Training is an afterthought

Staff need to know what the AI output means, what it does not mean, and what to do when it looks wrong.

A practical checklist

• Define the use case and the expected output.
• Confirm the product is cleared or approved for that use.
• Test on local studies with radiologist review.
• Assign ownership and monitoring.
• Train the people who will see the results.

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