Artificial intelligence in digital image processing: A bibliometric analysis

Abstract

This study presents a bibliometric analysis of artificial intelligence (AI) in digital image processing (DIP), analyzing 1063 publications from the Scopus database from 1998 to 2023. The field has seen significant growth, with an average annual growth rate of 16.24%, accelerating sharply between 2020 and 2023. The analysis emphasizes AI’s growing influence in healthcare and real-time image processing. China leads in publication volume, while the USA dominates in citation impact, underscoring the global and collaborative nature of AI-DIP research. Key institutions like the University of California and Tsinghua University, along with authors such as U. Rajendra Acharya, have made significant contributions to AI-driven healthcare diagnostics, highlighting the importance of interdisciplinary collaboration. High-impact journals, including IEEE Transactions on Medical Imaging, play a crucial role in advancing the field. However, this study relied on a targeted keyword search in Scopus, which may not capture all relevant research, particularly those using alternative terminologies or broader AI classifications. Additionally, challenges related to data privacy, bias, and transparency persist. Addressing these issues will be critical for the responsible development and application of AI-DIP technologies. This study offers valuable insights for future research and highlights key areas for continued exploration. • AI in DIP has grown significantly over the past 25 years. • Healthcare and real-time processing are key trends, driven by interdisciplinary collaboration. • China leads in output, while the USA dominates in citation impact and global influence. • Ethical challenges include bias, data privacy, and transparency in AI driven systems. • This study provides insights into future trends and opportunities for AI in DIP research.

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