Working with medical data really changed how I think about annotation. Compared to general image or text labeling, the margin for error feels much smaller, and the consequences of inconsistency show up much faster.
Some things that stand out to me:
-
tiny annotation differences can change clinical meaning
-
edge cases are common, not rare
-
reviewer agreement is harder to maintain
-
QA needs to be much stricter than usual
A lot of problems don’t appear until later, when models are validated or retrained, which makes them harder to trace back to annotation decisions.
Looking at structured medical annotation workflows helped me understand where these issues usually come from and how teams try to control them:
https://aipersonic.com/medical-annotation/
Sharing this for context, not as a recommendation.
For those who’ve worked on medical AI projects:
-
how do you manage reviewer consistency?
-
do you rely fully on domain experts, or a mix of trained annotators + review?
-
what QA steps actually made a difference for you?
Would be great to hear real experiences.