10/28/2026
Your medtech AI model may be passing every test for the wrong reason.
When training and test images come from the same scanners, a model can learn to recognize the equipment instead of the abnormality it was built to detect. Accuracy looks strong until a new scanner or clinical site comes online, and by then the model is already in front of clinicians.
The FDA has called out this exact risk in its draft guidance on AI-enabled devices. For technical leaders, that puts the data pipeline under the same scrutiny as the model itself.
Finding the problem before launch is a design decision. Finding it after launch means working through revalidation and change control while the model is in use.
Our latest article explains how medtech teams can build pipelines that hold up beyond the test environment. Continue reading below.