Mapping the future of early breast cancer diagnosis: a bibliometric analysis of AI innovations

Abstract

Breast cancer (BC) remains one of the most prevalent and challenging malignancies worldwide, affecting millions of women and shaping healthcare priorities across continents. Advances in early detection have significantly improved survival rates. In recent years, artificial intelligence (AI) has emerged as a powerful tool in this domain, transforming traditional diagnostic methods. Initially based on simple rule-based systems, AI has evolved into sophisticated deep learning models capable of analyzing complex medical data with remarkable accuracy. This bibliometric analysis examines the application of AI in the early diagnosis of breast cancer, aiming to understand not only the current state of the field but also its growth over the past decade. Publications indexed in Web of Science and Scopus from 2012 to March 2025 were systematically reviewed, while earlier literature (1994-2012) provided historical context. Tools such as Biblioshiny and VOSviewer were used to map research trends, collaboration patterns, and thematic evolution. Out of 1,436 initial documents, 1,293 high-quality studies were included. The results show a clear acceleration in AI-focused research after 2020, with increased global collaboration and a notable shift toward open-access publication. Recurring themes such as "machine learning," "diagnostic imaging," and "clinical decision support" highlight the field's direction. As AI becomes more integrated into clinical workflows, its potential to enhance diagnostic speed, consistency, and personalization is undeniable. However, key ethical issues such as bias, transparency, and patient data protection remain central to responsible implementation.

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