Yes, the symbolic AI operates on symbols, but your idea surpasses what it really can do. It was mainly used for the expert system and the input was in the form of symbols, not just any data.
To feed the textbooks into the AI would require Natural Language Processing at higher level than currently is achievable. The symbolic AI concept was created about seventy years ago, so it cannot operate as you imagine.
On the other hand feeding appropriate data into network to give prediction could be used, but here inherent problem is with lack of full medical knowledge to make it work.
If the goal is to check textbooks and return relevant information then the task is simpler, here dictionary based search would do the trick.
No, the raw data is being fed to NN to solve some optimization / classification / estimation problem based on provided data (unsupervised or supervised learning on the training set).
It doesn't create implicit knowledge, think about it more in the terms of regression or approximation. Currently it is not fully known what exactly is the state of the knowledge of trained network, we are not sure what it has learned.
If you compare the expert system and trained classifier, they do not have similarities. The expert system deduce based on given data, requests missing info, input is symbolic and extensible. For raw data approaches the network is not extensible in this sense, it requires new training samples and possibly new architecture if the task differs from the initial goal.