SynthesisFrontiers in oncology2026
Artificial intelligence in breast cancer research: a systematic review and bibliometric analysis of emerging trends and future directions.
Synthesis in Frontiers in 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
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Breast cancer remains a leading cause of cancer incidence and mortality among women worldwide, with ongoing challenges in early detection, accurate diagnosis, prognosis estimation, and treatment-response assessment. Artificial intelligence (AI), including machine learning and deep learning, has rapidly advanced breast cancer research by enabling analysis of high-dimensional imaging, pathological, genomic, and clinical data. However, the rapid expansion of this literature has resulted in substantial fragmentation across clinical tasks, data modalities, algorithmic paradigms, and software ecosystems. This systematic review presents a comprehensive bibliometric analysis of AI-driven breast cancer research published recently. Methods: Using a multi-database, Scopus and Web of Science, as primary data sources, a systematic bibliometric framework integrating performance analysis, science mapping, and thematic evolution was employed. The analysis is guided by explicit research questions examining: (i) the evolution of AI applications across diagnosis, prognosis, and treatment-response prediction; (ii) the adoption of explainable AI and hyperparameter optimization practices; (iii) the datasets, software frameworks, and algorithms shaping the field; (iv) the role of mammography as a core modality; and (v) emerging research hotspots, including transformer-based architectures. Results and Discussion: Unlike prior bibliometric studies focused mainly on citation metrics, this work adopts a task-aware, methodologycentric perspective, offering actionable insights to support reproducible, interpretable, and clinically integrated AI systems for breast cancer care.
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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.