ArticleTechnology and health care : official journal of the European Society for Engineering and Medicine2026
Bibliometric analysis of research on artificial İntelligence applications in breast cancer diagnosis.
Article in Technology and health care : official journal of the European Society for Engineering and Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- The Evolving Landscape of Deep Learning in Breast Cancer Imaging: A Bibliometric Study of Segmentation, Detection, and Diagnostic Research From 2006 to 2025.Cancer control : journal of the Moffitt Cancer CenterReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
ObjectiveThis analysis aims to examine studies on artificial intelligence (AI) applications in breast cancer diagnosis through bibliometric methods, focusing on temporal and geographical trends. It contributes to shaping the field's roadmap and helping researchers adapt to technological innovations.MethodA comprehensive search was conducted in the Web of Science (WOS) database. Bibliometric analyses of data from 2013-2024 were performed using VOSviewer and Bibliometrix R programs.ResultsThe analysis included 1537 articles. A significant rise in research activity was observed in 2019. The thematic analysis highlighted topics like histopathology, feature selection, deep learning, and machine learning. India was the most productive country with 405 studies. Keyword analysis showed increased usage of terms like transfer learning, CNN, and radiomics. U.S. was the most cited country with 7511 citations. Concept co-occurrence analysis revealed strong associations between terms such as feature selection, datasets, algorithm performance, and classification methods. Bejnordi's 2017 study was identified as the most influential, with 1909 citations.Discussion and ConclusionThis study identifies key authors, influential works, and trending topics, offering a broad understanding of the field's structure and evolution. It helps outline the advancements and emerging directions in AI applications for breast cancer diagnosis.
Indexed as
Identifiers
What OpenQuestion holds
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.