It is getting easier to scale deep learning in difficult domains. It'll likely be some combo of pretrained semi- or self-supervised models that are transferred

It is getting easier to scale deep learning in difficult domains. It'll likely be some combo of pretrained semi- or self-supervised models that are transferred and fine-tuned. You mention large data, but we also have the knobs of inductive bias and training objectives. Once we crack inductive biases for CT scans, perhaps by analyzing a model trained on large amounts of data, then scaling gets easier. I don't think the situation is too dire, it's just a very difficult and high-risk domain. It could also get a lot easier once we figure out better training objectives, but just like inductive biases, those are pretty domain specific so they take some time to discover.