The Role of Synthetic Data and Generative AI in Breast Imaging: Promise, Pitfalls, and Pathways Forward
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View abstract on PubMed
Summary
This summary is machine-generated.Synthetic data and generative AI can improve breast imaging AI performance and training. However, careful validation and transparent reporting are crucial for responsible clinical use and to prevent bias.
Area Of Science
- Medical Imaging
- Artificial Intelligence
- Data Science
Background
- Artificial intelligence (AI) is transforming breast imaging.
- Progress is hindered by data scarcity, privacy concerns, and representation issues.
Purpose Of The Study
- To review synthetic data and generative AI (GANs, diffusion models) in mammography.
- To assess their impact on data augmentation, bias mitigation, and performance gains.
- To analyze validity threats and outline responsible adoption guidelines.
Main Methods
- Narrative review of evidence from 2020 to April 2025.
- Synthesis of studies on synthetic image generation and application in mammography.
- Analysis of AI bias, validity, and regulatory considerations.
Main Results
- Synthetic images aid data augmentation, class balancing, validation, and training.
- Reported improvements in detection performance are noted.
- Potential for bias mitigation or amplification across subgroups was assessed.
Conclusions
- Synthetic data and generative AI offer potential for enhanced AI performance, training, and data sharing in breast imaging.
- Responsible clinical adoption necessitates rigorous validation, transparency, and clear governance.
- Addressing validity threats and medico-legal risks is essential for safe implementation.
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