e22553 Background: Breast cancer (BC) is the second leading cause of cancer-related deaths among women in the US. Advances in screening converge with an improved understanding of tumor biology, germline genetics, and immuno-oncology to reshape screening from population-based imaging to precision, risk-adapted early detection. BC screening rates among women in the US is 80%, and 66% are diagnosed at localised stages; however, access remains socio-economically inequitable. This review identified trends in the current landscape, and future directions of early BC screening. Methods: A PubMed search for peer-reviewed publications from August 31, 2019, to August 31, 2024, that evaluated BC screening methods was conducted. Qualitative thematic analysis identified recurring themes and research drivers. Research areas were defined by the numbers of publications, the increase in publications over time, and expert input. Results: Of 9094 publications identified, 1530 were excluded, and 7564 were included in the literature analysis. Recurring themes and research drivers included breast density (8%), risk-informed strategies (notably hereditary risk) and personalization (7%), accessibility/equity (5%), screening efficiency (3%), and overdiagnosis (1%). Prominent cross-cutting themes were the integration of biological insights, particularly germline susceptibility, tumor heterogeneity, and circulating biomarkers, into strategies intended to improve early detection and screening efficiency. Emerging modalities with high publication prevalence and growth included AI, blood-based biomarker analyses, abbreviated magnetic resonance imaging (MRI) protocols, and contrast-enhanced MRI. Emerging innovations like abbreviated MRI and portable or wearable technologies aim to improve accessibility, comfortability, and cost-effectiveness. Conclusions: Ongoing research is shifting BC screening to biologically informed, risk-adapted pathways that distinguish aggressive from indolent disease, enabling escalation or de-escalation based on breast density, hereditary risk, tumor biology, and comorbidities. AI integration may reduce diagnostic errors, minimize non-biased interpretation time, and improve personalization and accessibility, thereby supporting radiologists in clinical decision-making. Integrating genomics, ancestry-aware risk models, and AI-enabled interpretation is essential to ensure that advances in screening improve outcomes equitably. Funding: This literature search was funded by AstraZeneca plc. Acknowledgments: Medical writing support, under the direction of the authors, was provided by Joshua Quartey of Real Chemistry, and was funded by AstraZeneca plc, in accordance with Good Publication Practice (GPP 2022) guidelines (Ann Intern Med 2022;175:1298–304).
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