ReviewBreast cancer (Tokyo, Japan)2025
Global mapping of artificial intelligence applications in breast cancer from 1988-2024: a machine learning approach.
Review in Breast cancer (Tokyo, Japan), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Interpretable machine learning for prognostic prediction in young women with triple-negative invasive ductal breast cancer: a Boruta-SHAP integrated approach.Gland surgery · 2026Article
- National and Subnational Burden of Female Breast Cancer in Iran from 2010 to 2021.Diseases (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundArtificial intelligence (AI) has become increasingly integral to various aspects of breast cancer care, including screening, diagnosis, and treatment. This study aimed to critically examine the application of AI throughout the breast cancer care continuum to elucidate key research developments, emerging trends, and prevalent patterns.
methodsEnglish articles and reviews published between 1988 and 2024 were retrieved from the Web of Science database, focusing on studies that applied AI in breast cancer research. Collaboration among countries was analyzed using co-authorship networks and co-occurrence mapping. Additionally, clustering analysis using Latent Dirichlet Allocation (LDA) was conducted for topic modeling, whereas linear regression was employed to assess trends in research outputs over time.
resultsA total of 8,711 publications were included in the analysis. The United States has led the research in applying AI to the breast cancer care continuum, followed by China and India. Recent publications have increasingly focused on the utilization of deep learning and machine learning (ML) algorithms for automated breast cancer detection in mammography and histopathology. Moreover, the integration of multi-omics data and molecular profiling with AI has emerged as a significant trend. However, research on the applications of robotic and ML technologies in surgical oncology and postoperative care remains limited. Overall, the volume of research addressing AI for early detection, diagnosis, and classification of breast cancer has markedly increased over the past five years.
conclusionsThe rapid expansion of AI-related research on breast cancer underscores its potential impact. However, significant challenges remain. Ongoing rigorous investigations are essential to ensure that AI technologies yield evidence-based benefits across diverse patient populations, thereby avoiding the inadvertent exacerbation of existing healthcare disparities.
Indexed as
Identifiers
41016987What 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.