You could use Topic Modeling as described in this paper:
They performed Topic Modeling on abstracts of patents (limited to 150 words). They identified papers as "novel" if they were the first to introduce a topic, and measured degree of novelty by how many papers in the following year used the same topic. (Read the paper for details).
I suggest that you follow their lead and only process paper abstracts. Processing the body of each paper might reveal some novelty that the abstract does not, but you also run the risk of having much more noise in your topic model (i.e. extraneous topics, extraneous words).
While you say that all 500 papers are on the same "topic", it's probably safer to say that they are all on the same "theme" or in the same "sub-category" of Bio-medicine. Topic modeling permits decomposition of the "theme" into "topics".
The good news is that there are plenty of good packages/libraries for Topic Modeling. You still have to do preprocessing, but you don't have to code the algorithms yourself. See this page for many resources: