Mapping the evolving landscape of artificial intelligence in pathology: A bibliometric analysis of research trends and emerging frontiers (2009-2025)
Ke Chai, Kun Wang, Canbin Chen, Shan Zeng, Jinming Zhang, Xiaguang DuanBackground
Artificial intelligence (AI) has become a major methodological driver in computational pathology, but the field’s temporal growth, collaboration structure, intellectual foundations, and emerging translational priorities remain incompletely mapped.
Objective
To characterize the development of AI-related computational pathology from 2009 to 2025 and identify literature-level signals of research maturation, knowledge structure, and emerging frontiers.
Methods
The Science Citation Index Expanded within the Web of Science Core Collection was searched for AI-related computational pathology publications from 1 January 2009 to 31 December 2025. The initial Topic Search identified 2,480 records; after document-type and language filtering, 2,216 English-language articles and reviews were included. CiteSpace, VOSviewer, and bibliometrix were used to analyse publication trends, collaboration networks, citation and co-citation structures, keyword co-occurrence, and burst patterns. Model-fit, network-structure, and cluster-quality metrics were used to support temporal and network interpretation.
Results
Annual publications increased from 1 in 2009 to 540 in 2025, with cumulative citations reaching 20,910. A logistic model provided the primary descriptive fit for publication growth (R 2 = 0.990; adjusted R 2 = 0.988), capturing rapid post-2019 expansion with attenuation of the growth rate. The United States led in publication output and co-authorship connectivity, China showed rapid volume growth, and the United Kingdom had the highest country-level betweenness centrality. Co-citation and journal analyses indicated an interdisciplinary knowledge base linking computational imaging, pathology, oncology, and biomedical research. Keywords and bursts shifted from classification and segmentation toward analysis of whole-slide images, transformers, biomarker inference, prognostic modelling, multimodal integration, and workflow-oriented research questions.
Conclusions
AI-related computational pathology has developed into a rapidly expanding and interdisciplinary literature. These findings are bibliometric signals of scholarly development, not evidence of clinical effectiveness or real-world implementation. Future work should prioritize externally validated, interpretable, calibrated, workflow-compatible, and ethically governed AI systems across diverse pathology settings.