From Fragmentation to Integration: A Bibliometric Analysis of Generative AI Applications in Quality Management

The research area of Generative Artificial Intelligence (GenAI) is Quality Management (QM) that is yet to be systematized, and there is little effort currently undertaken to organize the existing knowledge. This paper addresses this gap by performing a literature review to search 29 peer-reviewed articles published in 2014–2025 in Q1-Q2 articles listed in Scopus. Bibliometric analysis offers a summary of the patterns of publications, patterns of dissemination, influential authors, and the intellectual structure based on the analysis of bibliographic coupling. Its results show that the number of publications grew considerably since 2021, and citing activity took place in 2024–2025, and this is connected to the novelty and topicality of the topic. The logical synthesis of the disjointed studies included in this paper results in the conceptualization of the notions concerning the intellectual patterns and conceptual frameworks and the implementation of the application of GenAI in the context of the inspection, anomalies detection, and predictive quality strategies. It also determines crucial research gaps, standard benchmarks, reproducibility protocols, and governance systems to have the capacity to empower the responsible and scalable implementation of GenAI to QM.