Bibliometric analysis of artificial intelligence in medical imaging for hospital performance and regulation

Abstract The aim of this paper is to conduct a bibliometric analysis of the international literature on artificial intelligence (AI) applied to medical imaging published between 2010 and 2024. The analysis is based on a corpus of 2940 documents retrieved from the Scopus database, revealing a rapid annual growth rate of 41.95% and extensive international collaboration patterns. Specifically, the aim of the literature review is to analyze the interdependencies on the conceptualization of performance expectations of hospital organizations and the related regulatory and governance aspects. During the collecting stage, the data were downloaded from the Scopus database according to the SPAR-4-SLR method and analyzed using Biblioshiny (Bibliometrix package in R) and VOSviewer. The findings highlight the evolution of the field from algorithm-centric research toward implementation-oriented discussions integrating hospital organizational performance and regulatory governance. Two major research streams were identified: organizational performance and governance/regulation. In contrast to earlier bibliometric studies that primarily concentrated on the technical performance of AI algorithms, the present study contributes to the literature by incorporating organizational performance expectations and regulatory governance as key analytical dimensions in examining the implementation of AI in medical imaging. Nevertheless, this study does not aim to evaluate the direct impact of AI on hospital performance.