Go is a much harder problem in large part because it's really hard to accurately classify which board is better off.
Teaching Deep Convolutional Neural Networks to Play Go: "Our convolutional neural networks can consistently defeat the well known Go program GNU Go... It is also able to win some games against state of the art Go playing program Fuego while using a fraction of the play time." http://arxiv.org/abs/1412.3409
anyways, that's not my point. i'm saying deep learning for image analysis has been a huge success, and people should explore ways to apply this success to medical imaging.
but just for your info, neural networks are slowly being generalized to a lot of other challenges. look around for topics on recurrent neural networks, memory networks, reinforcement learning, etc etc. i don't think we've fully finished exploring the many ways neural networks can help solve life's challenges just yet.
You can say that about almost anything, and the world is still full of factory workers.
As a PhD student in medical imaging, you must also know that getting fully automating segmentation methods to work to the standard required in the clinic is really hard. And once you solve it for one clinic you will likely not be able to transfer the trained model to another clinic, because scan parameters, patients and workflow are different.
But when we solve the segmentation task, I think most radiologist will clap their hands and move on.