To be fair, you can get really very far with non-machine-learning automated techniques (I did good-enough algorithms for an in-situ fluorescent gene sequencing image processing pipeline at one job). I suspect any form of good automated processing, regardless of whether it's AI, would be welcome to biologists.

If you'd ever like to chat about the automation parts, I'd be interested in hearing how you're approaching it; the niche of "scientific computing, but repeatable" is quite different than traditional scientific software, and it seems like people are still in early stages of figuring out how to do it.

Would also be interested in hearing how you approach correctness. The best approach I've discovered is metamorphic testing. Basically you modify real inputs, and then ensure the output matches. E.g. you say, "OK, I have this finished algorithm that segments cells, `f(image) -> cells`. Now, if I double brightness on everything, I would expect the same results, so let's see what `f(brighter_image)` is, I would expect same output." Or like "If I merge nuclei-looking splotches that cross what original segmentation boundary was, that should result in fewer cells." Unfortunately only discovered this technique after I left the image processing job, so haven't had chance to try it.