Artículo

Artificial intelligence in digital image processing: A bibliometric analysis

Resumen

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.
Sganzerla, William Gustavo (57209267807); da Silva, Aline Priscilla Gomes (57022859700)
Uvaia (Eugenia pyriformis Cambess – Myrtaceae): An overview from the origin to recent developments in the food industry – A bibliometric analysis
2022
10.1016/j.jafr.2022.100369
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136526160&doi=10.1016%2fj.jafr.2022.100369&partnerID=40&md5=f0cbeb0c39514aa9ed78896e1f072298
All Open Access; Gold Open Access
Scopus
Artículo obtenido de:
Scopus
Fuente
0 0 votos
Califica el artículo
Subscribirse
Notificación de
Campus Samborondón
Vía Samborondón Km. 13.5
Call Center: 5150400
metricas@ecotec.edu.ec
Facebook Instagram Youtube

Mapa del sitio