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[332] - Artificial Intelligence and Open Science: A Bibliometric Analysis of Recent Scientific Production ABSTRACT Objective: To map and analyze the scientific literature on the intersection between Artificial Intelligence and Open Science, identifying themes, collaboration networks, and intellectual foundations that shape the field. Methodology: A quantitative bibliometric study using data from the OpenAlex platform (August 2025), comprising 447 open-access articles. The analysis was conducted via VOSviewer, focusing on conceptual (keyword co-occurrence), intellectual (co-citation and bibliographic coupling), and social (co-authorship) structures. Results: The field has grown exponentially since 2023, with a core in "computer science," "artificial intelligence," and "data science," expanding into health, environment, and bioinformatics. The temporal evolution indicates a maturation towards ethical and governance discussions ("scientific publishing," "research integrity"). Production and collaboration are dominated by the Global North. Discussion: Although technically and computationally driven, the field focuses on the implications of AI for scientific practice and integrity. The Anglophone predominance and the concentration of research in the Global North raise questions about diversity and inclusion, which are fundamental pillars of Open Science principles. Conclusions: Research at the intersection of Artificial Intelligence and Open Science is multifaceted and growing, driven by robust international collaboration. Policies and incentives are needed to foster open practices, promoting a more transparent, collaborative, fair, and equitable scientific ecosystem, and addressing the need for greater global equity in research.