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Updated: Aug 24, 2025
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
Published on: July 11, 2025
Research progress in digital pathology: A bibliometric and visual analysis based on Web of Science
Jinjin Zhao1, Zhengqi Han2, Yixun Ma1
1Key Laboratory of Evidence Science, Institute of Evidence Law and Forensic Science, China University of Political Science and Law, Ministry of Education, Beijing, China; Collaborative Innovation Center of Judicial Civilization, Beijing, China.
Background:
The development of whole slide image and deep neural network technologies has contributed to the paradigm shift in diagnostic pathology and has received much attention from researchers, with related publications increasing yearly and "exploding" in recent years. However, few studies have systematically reviewed "digital pathology" using bibliometric tools. In this study, we will use multiple approaches to visualize and analyze "digital pathology" to provide a comprehensive and objective picture of the field's historical evolution and future development.
Methods:
We use VOSviewer, CiteSpace, Gephi, and R to analyze the authors, institutional and national collaboration networks, keyword co-occurrence, and co-citation analysis to visualize the current status of global digital pathology research.
Results:
Digital pathology-related research is mainly active in "molecular, biological, and immunology" journal groups, "pharmaceutical, medical, and clinical" journal groups, and "psychology, education, and health" journal groups; in addition to "digital pathology," "diagnosis," "deep learning," "histopathology," and "surgical pathology" are also active research topics; the U.S. has significant research results in digital pathology, with the top 10 publishing institutions all coming from the U.S. In the past two decades, global digital pathology-related research can be divided into two major research areas. One is about system verification and optimization of WSI, and the other is about the application and development of artificial intelligence technology in digital pathology. Among them, based on the development of computer technology and the update of the machine learning concept, the research results for deep neural network technologies have been more concentrated in recent years. The robust performance of deep neural networks in feature extraction and image analysis provides a new research direction for improving digital pathology-aided diagnosis systems, which is where the research hotspots have been in recent years.
Conclusions:
The bibliometric analysis may help better understand the current status of research within the field of digital pathology and provide references and lessons for future related research.
