Review articles in scientific journals are a helpful way for researchers to learn the state of the art in a field from leading names in the field. In the field of cancer treatment, there are many such review articles in the literature. One common issue with review articles is that they are almost immediately out of date, that is, as soon as a new article is published with new data that changes the scientific consensus about a problem, the review article will not have this information. In addition, many review articles are published in prestigious journals, which means that they are often expensive and thereby inaccessible to a large part of the world. We have sketched out an AI in this hackathon that writes up to date literature reviews from the very latest literature, downloaded fresh from Entrez PubMed, a vast archive of biological abstracts, with millions of peer-reviewed abstracts about cancer. We use this information along with Retrieval Augmented Generation to generate sentences for a new article, where each sentence is based on one from a chosen literature review article.
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