Mapping the evolving landscape of artificial intelligence in pathology: A bibliometric analysis of research trends and emerging frontiers (2009-2025)
Year: 2026
Type: article
Abstract: Background: 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: = 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.
Source: Digital Health
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,000
Awards: G1247672163, G2013111734
OpenAlex ID: W7166523293