Generative Artificial Intelligence for Medical Imaging Synthesis and Augmentation from 2017 to 2026: A Systematic Review of Diffusion Models, GANs, and VAEs for MRI, CT, X-Ray, and Pathology
Abstract
Generative artificial intelligence (AI), including GANs, VAEs, and diffusion models, is increasingly used for synthesizing and enhancing medical images, helping address challenges such as limited data, expensive acquisition, and rare disease representation.This systematic review examines studies on generative AI methods for MRI, CT, Xray, and pathology image synthesis from 2017 to 2026, focusing on synthesis tasks, evaluation strategies, and clinical utility.A PRISMA 2020-compliant search of PubMed, IEEE Xplore, Scopus, and Web of Science identified peerreviewed research on generative models for medical image synthesis, augmentation, harmonization, or cross-modality translation.Findings show a shift from GAN-based methods to diffusion models post-2022, with MRI and CT studies emphasizing cross-modality translation, and X-ray and pathology studies focusing on augmentation and diagnostic utility.Despite GANs' continued dominance, diffusion models are gaining traction for improving image fidelity and diversity.However, evaluation practices remain inconsistent, with limited inclusion of clinically relevant assessments.This review follows PRISMA 2020 guidelines and provides a narrative synthesis of the evidence.1 2 Medical imaging increasingly depends on large, diverse, and well-annotated datasets, yet many clinical tasks are constrained by limited patient numbers, rare pathologies, privacy restrictions, heterogeneous scanners, and high annotation costs.Generative artificial…
How strong is the evidence?
Evidence Hub reads this as a systematic review. 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.