Automation bias has two forms. Commission errors are accepting a wrong recommendation. Omission errors are failing to notice something the system did not flag. Both are well documented across aviation, industry, and medicine.

In medical imaging it manifests as reduced detection of findings the software did not mark, because an unmarked region acquires an implicit and unearned reassurance.

Where This Gets Misread

This is the mechanism that turns a technically accurate tool into a clinical harm. The model does not have to be wrong for outcomes to worsen ... it only has to change how carefully the clinician looks. It is also the reason a device that improves standalone accuracy can fail to improve reader performance in practice.