Related Experiment Video
Updated: Jun 14, 2025
Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
Published on: April 18, 2025
Global bibliometric mapping of the research trends in artificial intelligence-based digital pathology for lung cancer
Dan-Dan Xiong1,2, Rong-Quan He3, Zhi-Guang Huang1
1Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Background And Objective:
The rapid development of computer technology has led to a revolutionary transformation in artificial intelligence (AI)-assisted healthcare. The integration of whole-slide imaging technology with AI algorithms has facilitated the development of digital pathology for lung cancer (LC). However, there is a lack of comprehensive scientometric analysis in this field.
Methods:
A bibliometric analysis was conducted on 197 publications related to digital pathology in LC from 502 institutions across 39 countries, published in 97 academic journals in the Web of Science Core Collection between 2004 and 2023.
Results:
Our analysis has identified the United States and China as the primary research nations in the field of digital pathology in LC. However, it is important to note that the current research primarily consists of independent studies among countries, emphasizing the necessity of strengthening academic collaboration and data sharing between nations. The current focus and challenge of research related to digital pathology in LC lie in enhancing the accuracy of classification and prediction through improved deep learning algorithms. The integration of multi-omics studies presents a promising future research direction. Additionally, researchers are increasingly exploring the application of digital pathology in immunotherapy for LC patients.
Conclusions:
In conclusion, this study provides a comprehensive knowledge framework for digital pathology in LC, highlighting research trends, hotspots, and gaps in this field. It also provides a theoretical basis for the application of AI in clinical decision-making for LC patients.
