Staging the Integration of Generative Neural Networks: A Comparative Analysis of Scientific, Educational and Medical Activities

Introduction. The emergence of publicly available generative neural networks has become one of the most significant factors in modern intellectual labor. Unlike previous generations of digital technologies, generative models are directly incorporated into the production of texts, images, program code, analytical materials, and other intellectual work results. The aim of the study was to conduct a comparative sociological analysis of how generative neural network technologies are implemented into scientific, educational and medical activities, as well as to identify the institutional differences that shape their professional development. Materials and methods. The research is theoretical and analytical in nature. Methods of comparative, institutional, historical genetic, and structural and functional analysis have been applied. Results. Generative neural networks are evolving from experimental algorithmic solutions to widespread professional tools and objects of institutional policy. To analyze this dynamics, a four-stage model has been proposed, including the protogenerative stage (2010-2014), the generative stage (2014-2018), the stage of platform consolidation (2018–2022), and the stage of mass institutionalization (2022-present). This model is based on a staging concept on scientific and educational activities, developed by the author, through its use in the comparative analysis of the medical field.