Bibliometric Analysis AI-Based Decision Analytics

Abstract

The rapid advancement of artificial intelligence (AI) has transformed decision-making processes across various domains by enabling data-driven insights, predictive capabilities, and intelligent automation. This study aims to examine the development, intellectual structure, and emerging trends of research on AI-based decision analytics through a bibliometric analysis approach. Data were collected from the Scopus database using relevant search terms related to artificial intelligence and decision analytics. The study applies bibliometric techniques, including performance analysis, citation analysis, co-authorship analysis, keyword co-occurrence analysis, thematic evolution analysis, and density visualization using VOSviewer. The findings indicate that artificial intelligence, machine learning, deep learning, and clinical decision support systems represent the dominant research themes shaping this field. Highly cited studies demonstrate increasing scholarly attention toward ethical considerations, explainability, transparency, and trust in AI-driven decision systems. The collaboration analysis reveals that research development is supported by extensive international networks, with countries such as Germany, the United States, India, and China serving as influential contributors. Furthermore, the temporal analysis indicates a shift from algorithm-focused research toward human-centered and responsible AI applications. This study contributes to the literature by providing a comprehensive mapping of AI-based decision analytics research and identifying future directions related to explainable AI, trustworthy decision systems, and interdisciplinary applications across healthcare, business, and other complex decision environments.