ArticleDigital health
Mapping the evolving landscape of artificial intelligence in pathology: A bibliometric analysis of research trends and emerging frontiers (2009-2025).
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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: 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 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.