  Background: The emergence of generative AI tools, empowered by Large Language
Models (LLMs), has shown powerful capabilities in generating content. To date,
the assessment of the usefulness of such content, generated by what is known as
prompt engineering, has become an interesting research question. Objectives
Using the mean of prompt engineering, we assess the similarity and closeness of
such contents to real literature produced by scientists. Methods In this
exploratory analysis, (1) we prompt-engineer ChatGPT and Google Bard to
generate clinical content to be compared with literature counterparts, (2) we
assess the similarities of the contents generated by comparing them with
counterparts from biomedical literature. Our approach is to use text-mining
approaches to compare documents and associated bigrams and to use network
analysis to assess the terms' centrality. Results The experiments demonstrated
that ChatGPT outperformed Google Bard in cosine document similarity (38% to
34%), Jaccard document similarity (23% to 19%), TF-IDF bigram similarity (47%
to 41%), and term network centrality (degree and closeness). We also found new
links that emerged in ChatGPT bigram networks that did not exist in literature
bigram networks. Conclusions: The obtained similarity results show that ChatGPT
outperformed Google Bard in document similarity, bigrams, and degree and
closeness centrality. We also observed that ChatGPT offers linkage to terms
that are connected in the literature. Such connections could inspire asking
interesting questions and generate new hypotheses.
