ReviewDiscover oncology2026
A review of application of Artificial Intelligence in breast cancer detection and treatment.
Review 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
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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
AI has revolutionized breast cancer care. It enhances diagnostic accuracy, improves early detection, and personalizes treatment while reducing unnecessary testing. Thus, it is crucial to examine the incorporation of AI to emphasize its potential benefits for diagnostic precision, screening optimization, and overall patient outcomes within clinical workflows. Utilizing bibliometric analysis, this research identifies significant contributions and landmark studies that have driven advancements in AI applications for breast cancer. This analysis further highlights trending themes and technologies, which are vital for guiding future research initiatives and aligning resource allocation with the most urgent challenges in the field. The research analyses ninety-seven articles concerning AI applications in the detection and treatment of breast cancer, which are indexed in the Web of Science, for the purpose of identifying themes and conducting analysis. The initial section presents a comprehensive overview of research growth, leading authors, key publishers and journals contributing to the literature. Subsequently, an analysis of major studies related to AI in the detection and treatment of breast cancer have been provided. There has been a marked upward trend in research output on AI applications in breast cancer in 2023 following a gradual rise over the past 25 years. Future research directions in AI for breast cancer should emphasize enhancing algorithm accuracy, integrating multi-modal imaging data, and improving real-time analytical capabilities. Additionally, further exploration of AI’s role in personalized treatment plans and patient monitoring could substantially improve clinical outcomes and optimize healthcare resources.
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.