ArticleDiscover oncology2026
Evolution of artificial intelligence in breast cancer pathology: a bibliometric analysis.
Article in Discover oncology, 2026. 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
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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.
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Authors and funding
6 authors.
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Abstract
backgroundBreast cancer is the most common malignant tumor affecting women, and pathology serves as the primary method for its diagnosis. In recent years, artificial intelligence (AI) has increasingly been applied to the pathological diagnosis of breast cancer. This study, therefore, aims to conduct a bibliometric analysis of the literature on artificial intelligence in breast cancer pathology (AIBCP) to map the research landscape, identify key trends, and highlight emerging directions.
methodsA total of 1089 relevant publications from 2011 to 2025 were retrieved from the Web of Science (WOS) database. Bibliometric analysis was conducted using bibliometric VOSviewer, and CiteSpace, while BERTopic modeling was applied to extract semantic topics from abstracts. The study examined publication trends, geographical and institutional contributions, author and journal profiles, citation patterns, references, as well as keywords and abstract-based topics.
resultsResearch on AIBCP has grown substantially since 2017, with 2025 recording the highest number of publications. The majority of them are original articles (70.25%). The leading contributing countries and regions are the USA, China, India, and the European Union, while the University of Toronto emerged as the most productive institution. Key influential authors include Jeroen van der Laak and Nasir Rajpoot. The most cited study focused on deep learning for lymph node metastasis detection. Fourteen distinct research themes were identified, with Topic 0, centered on deep learning for histopathology image analysis, being the dominant topic (53.54%). Other topics included mitosis detection and analysis, tumor microenvironment and prognosis, spatial transcriptomics and gene expression, natural language processing, and multimodal and explainable AI models. Topic analysis reveals a shift from unimodal image analysis towards multimodal "pathomics" and emerging interdisciplinary areas.
conclusionThe field of AIBCP is rapidly evolving, driven by deep learning-based pathological image analysis and increasingly expanding into comprehensive multimodal and clinical research. Future directions should focus on addressing the AI "black box" problem, to improve interpretability, advancing multimodal approaches, and optimizing human-AI collaboration frameworks. This bibliometric analysis provides a foundational resource to guide researchers and clinicians in navigating and advancing the field.
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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.