Year: 2026
Type: review
Study design: Systematic Review
Abstract: Purpose: We aim to present a consolidated overview of current achievements in generative artificial intelligence (AI) in breast imaging, identify prevalent technical and clinical challenges alongside the strategies employed to overcome them, and highlight critical directions for future investigation. Approach: A comprehensive literature search has been conducted in PubMed, MEDLINE, and Embase for peer-reviewed journal publications since 2020, with the final search performed in October 2025. The review focuses on common methodologies, performance metrics, and strategies used to address existing hurdles in breast imaging. Results: The 120 included papers were categorized by application, ranging from technical and/or clinical tasks-image synthesis (including 24 data augmentation, 10 enhancement, 20 image-to-image translation, and 2 secure data sharing papers), 12 segmentation, 5 reconstruction, and 16 classification papers-as well as 17 papers in radiology workflow improvement, 13 papers in the development of medical education materials, and 7 papers on solving beyond our defined tasks (six papers were assigned more than one application). Papers in each category showed their own potential to solve the task of interests. However, we identified six common challenges and concerns, such as lack of standardized evaluation for synthetic outputs, across papers that the future research should be focused on. Conclusions: Recent works across the field of generative AI in breast imaging have demonstrated transformative potential in clinical workflows. However, there still exists technical and ethical gaps to be addressed for safe, reliable, and equitable deployment of the generative AI models to routine medical practice.
Source: Journal of medical imaging
Authors: Yen-Tung Liu, Juhun Lee
Institution: University of Pittsburgh
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W7225418594