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Bibliometric and Scientometric analyses offer invaluable perspectives on thecomplex research terrain and collaborative dynamics spanning diverse academicdisciplines. This paper presents pyBibX, a python library devised to conductcomprehensive bibliometric and scientometric analyses on raw data files sourcedfrom Scopus, Web of Science, and PubMed, seamlessly integrating state of theart AI capabilities into its core functionality. The library executes acomprehensive EDA, presenting outcomes via visually appealing graphicalillustrations. Network capabilities have been deftly integrated, encompassingCitation, Collaboration, and Similarity Analysis. Furthermore, the libraryincorporates AI capabilities, including Embedding vectors, Topic Modeling, TextSummarization, and other general Natural Language Processing tasks, employingmodels such as Sentence-BERT, BerTopic, BERT, chatGPT, and PEGASUS. As ademonstration, we have analyzed 184 documents associated with multiple-criteriadecision analysis published between 1984 and 2023. The EDA emphasized a growingfascination with decision-making and fuzzy logic methodologies. Next, NetworkAnalysis further accentuated the significance of central authors andintra-continental collaboration, identifying Canada and China as crucialcollaboration hubs. Finally, AI Analysis distinguished two primary topics andchatGPT preeminence in Text Summarization. It also proved to be anindispensable instrument for interpreting results, as our library enablesresearchers to pose inquiries to chatGPT regarding bibliometric outcomes. Evenso, data homogeneity remains a daunting challenge due to databaseinconsistencies. PyBibX is the first application integrating cutting-edge AIcapabilities for analyzing scientific publications, enabling researchers toexamine and interpret these outcomes more effectively.

pyBibX -- A Python Library for Bibliometric and Scientometric Analysis Powered with Artificial Intelligence Tools