Artificial intelligence in breast cancer research: a systematic review and bibliometric analysis of emerging trends and future directions

Abstract

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. 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. 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.