THE ECONOMIC DIMENSION OF ARTIFICIAL INTELLIGENCE USE IN HEALTHCARE: BIBLIOMETRIC MAPPING OF SCIENTIFIC PUBLICATIONS
TL;DR
The findings indicate that publications peaked in 2025, that the United States is the most productive country, and that artificial intelligence, machine learning, and deep learning are the most prominent research topics.
Abstract
The aim of this study is to analyze trends in the literature regarding the economic aspects of artificial intelligence use in the healthcare sector through bibliometric mapping of scientific publications and to present a comprehensive overview of the field’s current structure based on quantitative indicators. In this study, 1,326 studies obtained from a search conducted on February 15, 2026, in the Web of Science database using specified keywords were examined within the scope of bibliometric analysis using the VOSviewer program. The findings indicate that publications peaked in 2025, that the United States is the most productive country, and that artificial intelligence, machine learning, and deep learning are the most prominent research topics. Additionally, Olga Golubnitschaja, Wang Wei, and Carl Erb were identified as leading researchers in the field. Furthermore, the field was found to have a strong interdisciplinary structure, with research output being predominantly technology-focused and concentrated in high-impact-factor journals such as IEEE Access, Sensors, and Nature Communications; the most influential institutions were Stanford University, the National University of Singapore, and Harvard Medical School; and the most cited countries were the United States, India, and China. Furthermore, the United States, India, and China have stood out in terms of citation performance. Consequently, it is assessed that research on the economic impacts of AI in healthcare will continue to be important in the future.