Generative Artificial Intelligence (GAI) in Breast Cancer Diagnosis and Treatment: A Systematic Review
Source abstract
Abstract
This study systematically reviews the applications of generative artificial intelligence (GAI) in breast cancer research, focusing on its role in diagnosis and therapeutic development. While GAI has gained significant attention across various domains, its utility in breast cancer research has yet to be comprehensively reviewed. This study aims to fill that gap by synthesizing existing research into a unified document. A comprehensive search was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, resulting in the retrieval of 3827 articles, of which 31 were deemed eligible for analysis. The included studies were categorized based on key criteria, such as application types, geographical distribution, contributing organizations, leading journals, publishers, and temporal trends. Keyword co-occurrence mapping and subject profiling further highlighted the major research themes in this field. The findings reveal that GAI models have been applied to improve breast cancer diagnosis, treatment planning, and outcome predictions. Geographical and network analyses showed that most contributions come from a few leading institutions, with limited global collaboration. The review also identifies key challenges in implementing GAI in clinical practice, such as data availability, ethical concerns, and model validation. Despite these challenges, the study highlights GAI’s potential to enhance breast cancer research, particularly in generating synthetic data, improving diagnostic accuracy, and personalizing treatment approaches. This review serves as a valuable resource for researchers and stakeholders, providing insights into current research trends, major contributors, and collaborative networks in GAI-based breast cancer studies. By offering a holistic overview, it aims to support future research directions and encourage broader adoption of GAI technologies in healthcare. Additionally, the study emphasizes the importance of overcoming implementation barriers to fully realize GAI’s potential in transforming breast cancer management.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: AI in cancer detection · Radiomics and Machine Learning in Medical Imaging · Artificial Intelligence in Healthcare and Education
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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