| Abstract: |
Background: Breast 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. Methods: We 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). Results: Of 5774 studies identified, 264 met the inclusion criteria. The number of studies increased from 28 in 2017-2018 to 115 in 2022-2023 - 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. Conclusion: The 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. © 2025 The Authors |
