Global mapping of artificial intelligence applications in breast cancer from 1988-2024: a machine learning approach
Thi Huyen Trang Nguyen1, Somin Jeon2, Junghyun Yoon2
1Hanyang Institute of Bioscience and Biotechnology, Hanyang University, Seoul, Republic of Korea.
Background:
Artificial intelligence (AI) has become increasingly integral to various aspects of breast cancer care, including screening, diagnosis, and treatment. This study aimed to critically examine the application of AI throughout the breast cancer care continuum to elucidate key research developments, emerging trends, and prevalent patterns.
Methods:
English articles and reviews published between 1988 and 2024 were retrieved from the Web of Science database, focusing on studies that applied AI in breast cancer research. Collaboration among countries was analyzed using co-authorship networks and co-occurrence mapping. Additionally, clustering analysis using Latent Dirichlet Allocation (LDA) was conducted for topic modeling, whereas linear regression was employed to assess trends in research outputs over time.
Results:
A total of 8,711 publications were included in the analysis. The United States has led the research in applying AI to the breast cancer care continuum, followed by China and India. Recent publications have increasingly focused on the utilization of deep learning and machine learning (ML) algorithms for automated breast cancer detection in mammography and histopathology. Moreover, the integration of multi-omics data and molecular profiling with AI has emerged as a significant trend. However, research on the applications of robotic and ML technologies in surgical oncology and postoperative care remains limited. Overall, the volume of research addressing AI for early detection, diagnosis, and classification of breast cancer has markedly increased over the past five years.
Conclusions:
The rapid expansion of AI-related research on breast cancer underscores its potential impact. However, significant challenges remain. Ongoing rigorous investigations are essential to ensure that AI technologies yield evidence-based benefits across diverse patient populations, thereby avoiding the inadvertent exacerbation of existing healthcare disparities.
