ArticleEuropean journal of cancer (Oxford, England : 1990)2025
Global disparities in artificial intelligence-based mammogram interpretation for breast cancer: A scientometric analysis of representation, trends, and equity.
Article in European journal of cancer (Oxford, England : 1990), 2025. 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
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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.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- Reporting of race and ethnicity in studies of artificial intelligence in pediatric urology A secondary analysis of the AI-PEDURO online repository.Canadian Urological Association journal = Journal de l'Association des urologues du Canada · 2026Review
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Authors and funding
13 authors.
Funding
Abstract
backgroundBreast cancer (BC) is the most frequently diagnosed cancer and the leading cause of cancer death among women worldwide. Artificial intelligence (AI) shows promise for improving mammogram interpretation, especially in resource-limited settings. However, concerns remain regarding the diversity of datasets and the representation of researchers in AI model development, which may affect the models' generalizability, fairness, and equity.
methodsWe performed a scientometric analysis of studies published in 2017, 2018, 2022, and 2023 that used screening or diagnostic mammograms for BC detection to train or validate AI algorithms. PubMed (MEDLINE) and EMBASE were searched in July 2024. Data extraction focused on patient cohort sociodemographics (including age and race/ethnicity), geographic distribution (categorized by World Bank country income levels and regions), and author profiles (sex, affiliation, and funding sources).
resultsOf 5774 studies identified, 264 met the inclusion criteria. The number of studies increased from 28 in 2017 to 115 in 2023-a 311 % increase. Despite this growth, only 0-25 % of studies reported race/ethnicity, with most patients identified as Caucasian. Moreover, nearly all patient cohorts originated from high-income countries, with no studies from low-income settings. Author affiliations were predominantly from high-income regions, and gender imbalance was observed among first and last authors.
conclusionThe lack of racial, ethnic, and geographic diversity in both datasets and researcher representation could undermine the generalizability and fairness of AI-based mammogram interpretation. Addressing these disparities through diverse dataset collection and inclusive international collaborations is critical to ensuring equitable improvements in 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.