  This paper presents a comprehensive systematic review of generative models
(GANs, VAEs, DMs, and LLMs) used to synthesize various medical data types,
including imaging (dermoscopic, mammographic, ultrasound, CT, MRI, and X-ray),
text, time-series, and tabular data (EHR). Unlike previous narrowly focused
reviews, our study encompasses a broad array of medical data modalities and
explores various generative models. Our search strategy queries databases such
as Scopus, PubMed, and ArXiv, focusing on recent works from January 2021 to
November 2023, excluding reviews and perspectives. This period emphasizes
recent advancements beyond GANs, which have been extensively covered
previously.
  The survey reveals insights from three key aspects: (1) Synthesis
applications and purpose of synthesis, (2) generation techniques, and (3)
evaluation methods. It highlights clinically valid synthesis applications,
demonstrating the potential of synthetic data to tackle diverse clinical
requirements. While conditional models incorporating class labels, segmentation
masks and image translations are prevalent, there is a gap in utilizing prior
clinical knowledge and patient-specific context, suggesting a need for more
personalized synthesis approaches and emphasizing the importance of tailoring
generative approaches to the unique characteristics of medical data.
Additionally, there is a significant gap in using synthetic data beyond
augmentation, such as for validation and evaluation of downstream medical AI
models. The survey uncovers that the lack of standardized evaluation
methodologies tailored to medical images is a barrier to clinical application,
underscoring the need for in-depth evaluation approaches, benchmarking, and
comparative studies to promote openness and collaboration.
