  This paper discusses the effectiveness of leveraging Chatbot: Generative
Pre-trained Transformer (ChatGPT) versions 3.5 and 4 for analyzing research
papers for effective writing of scientific literature surveys. The study
selected the \textit{Application of Artificial Intelligence in Breast Cancer
Treatment} as the research topic. Research papers related to this topic were
collected from three major publication databases Google Scholar, Pubmed, and
Scopus. ChatGPT models were used to identify the category, scope, and relevant
information from the research papers for automatic identification of relevant
papers related to Breast Cancer Treatment (BCT), organization of papers
according to scope, and identification of key information for survey paper
writing. Evaluations performed using ground truth data annotated using subject
experts reveal, that GPT-4 achieves 77.3\% accuracy in identifying the research
paper categories and 50\% of the papers were correctly identified by GPT-4 for
their scopes. Further, the results demonstrate that GPT-4 can generate reasons
for its decisions with an average of 27\% new words, and 67\% of the reasons
given by the model were completely agreeable to the subject experts.
