A systematic review of generative artificial intelligence techniques for synthetic medical image datasets: Quality,...

Generative Artificial Intelligence (GAI) offers promising solutions to long-standing challenges in developing medical imaging methods and applications, including data scarcity, privacy concerns, and class imbalance. However, limited consolidation of publicly accessible synthetic datasets and trained GAI checkpoints restricts reproducibility and benchmarking. This systematic review aims to identify and evaluate such resources and assess their utility in clinical imaging applications.

We systematically searched PubMed, IEEE Xplore, and Scopus for studies published between January 2017 and June 2024. Eligible studies generated or used synthetic medical image datasets and publicly released either the dataset or the trained GAI model. Extracted data included imaging modality, dataset characteristics, model architecture, public availability, and evaluation strategy.

Of 941 screened records, 35 studies met inclusion criteria, comprising 37 publicly available resources spanning radiology (59%), pathology (16%), ophthalmology (14%), and dermatology (11%). Generative models included generative adversarial networks (73%), diffusion models (21%), autoencoders (3%), and hybrid architectures (8%). As some studies employed multiple model types, these categories are not mutually exclusive. Fifteen (43%) studies provided trained model checkpoints, enabling the generation of task-specific synthetic data. Evaluation methods included quantitative metrics, clinical expert assessment, and downstream performance in classification, segmentation, or detection tasks.

Although the reviewed resources support diverse downstream applications, publicly available synthetic datasets and trained models remain scarce. Evaluation strategies vary widely, and the absence of standardized benchmarks limits cross-study comparisons and reliability assessment. To support reproducibility and responsible use of GAI in medical imaging, future work should prioritize the public release of curated synthetic resources, clearer guidance on model selection, and standardized, multi-dimensional evaluation frameworks.