Multidisciplinary Research Mapping in Automation and Artificial Intelligence: A Bibliometric Analysis to Identify Science Convergence
DOI:
https://doi.org/10.58812/wsnt.v1i01.234Keywords:
Multidisciplinary, Research, Automation, AI, Bibliometric AnalysisAbstract
The field of Automation and Artificial Intelligence (AI) has witnessed rapid evolution, marked by interdisciplinary collaborations and groundbreaking advancements. This bibliometric analysis delves into the multidisciplinary research landscape within Automation and AI, aiming to identify science convergence and key trends. Utilizing a comprehensive dataset, we employed co-authorship analysis, citation analysis, keyword analysis, temporal analysis, and VOSviewer visualizations to map the dynamic landscape of Automation and AI research. Our analysis revealed extensive interdisciplinary collaboration among researchers from diverse domains, highlighting the role of cross-disciplinary innovation in advancing the field. Influential authors and highly cited papers were identified, emphasizing the impact of key contributions. Dominant research themes, such as machine learning, ethics in AI, and AI applications in healthcare, emerged from keyword analysis, reflecting the field's evolving priorities. VOSviewer visualizations provided clear representations of science convergence, showcasing the interconnectedness of disciplines like computer science, engineering, ethics, and economics. Interdisciplinary hubs and bridges were identified, underscoring the importance of cross-disciplinary research in shaping the future of Automation and AI. The findings of this analysis offer valuable insights for researchers, policymakers, and practitioners, providing a foundation for enhanced collaboration, ethical considerations, innovation in healthcare, and tailored education and training programs to meet evolving demands.
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