A systematic review of generative artificial intelligence techniques for synthetic medical image datasets: Quality, models, public availability and applications
Abstract
BACKGROUND AND OBJECTIVE: 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. METHODS: 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. RESULTS: 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…
How strong is the evidence?
Evidence Hub reads this as a systematic review, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
Read the paper
Related studies
- Learning from First-Generation Qualitative Approaches in the IS Discipline: An Evolutionary View and Some Implications for Authors and Evaluators (PART 1/2)
- Commentary on the clinical and preclinical dosage limits of interstitially administered magnetic fluids for therapeutic hyperthermia based on current practice and efficacy models
- Prosthesis: New Technological Opportunities and Innovative Biomedical Devices
- The history and future of neural modeling for cochlear implants
- Ryanodine receptors: waking up from refractoriness
- Novel Transgenic Rabbit Model Sheds Light on the Puzzling Role of Matrix Metalloproteinase-12 in Atherosclerosis
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.