Open Access

REVIEW

Generative Artificial Intelligence (GAI) in Breast Cancer Diagnosis and Treatment: A Systematic Review

Xiao Jian Tan^1,2,3,*, Wai Loon Cheor², Ee Meng Cheng^4,5, Chee Chin Lim^3,4, Khairul Shakir Ab Rahman⁶

1 Biomedical and Bioinformatics Engineering (BBE) Research Group, Centre for Multimodal Signal Processing (CMSP), Tunku Abdul Rahman University of Management and Technology (TAR UMT), Jalan Genting Kelang, Setapak, Kuala Lumpur, 53300, Malaysia
2 Department of Electrical and Electronics Engineering, Faculty of Engineering and Technology, Tunku Abdul Rahman University of Management and Technology (TAR UMT), Jalan Genting Kelang, Setapak, Kuala Lumpur, 53300, Malaysia
3 Sports Engineering Research Centre (SERC), Universiti Malaysia Perlis (UniMAP), Kampus Pauh Putra, Arau, 02600, Perlis, Malaysia
4 Faculty of Electronic Engineering and Technology, Universiti Malaysia Perlis (UniMAP), Kampus Pauh Putra, Arau, 02600, Perlis, Malaysia
5 Advanced Communication Engineering (ACE) Centre of Excellence, Universiti Malaysia Perlis (UniMAP), Kangar, 02600, Malaysia
6 Department of Pathology, Hospital Tuanku Fauziah, Jalan Tun Abdul Razak, Kangar, 01000, Malaysia

* Corresponding Author: Xiao Jian Tan. Email:

(This article belongs to the Special Issue: Advances in Artificial Intelligence and Generative AI: Impacts on Multidisciplinary Applications)

Computers, Materials & Continua 2025, 84(2), 2015-2060. https://doi.org/10.32604/cmc.2025.063407

Received 14 January 2025; Accepted 15 May 2025; Issue published 03 July 2025

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.

---

Keywords

---