A Scoping Review of Synthetic Data Generation for Biomedical Research and Applications
It addresses data scarcity, privacy, and quality issues in biomedical fields by synthesizing current practices, but is incremental as a review paper.
This scoping review analyzed 59 studies from 2020-2025 to examine trends in synthetic data generation for biomedical research, finding that unstructured texts (78.0%) were the most common data modality and prompting (72.9%) was the dominant method, with evaluations primarily using human-in-the-loop assessments (55.9%).
Synthetic data generation--mitigating data scarcity, privacy concerns, and data quality challenges in biomedical fields--has been facilitated by rapid advances of large language models (LLMs). This scoping review follows PRISMA-ScR guidelines and synthesizes 59 studies, published between 2020 and 2025 and collected from PubMed, ACM, Web of Science, and Google Scholar. The review systematically examines biomedical research and application trends in synthetic data generation, emphasizing clinical applications, methodologies, and evaluations. Our analysis identifies data modalities of unstructured texts (78.0%), tabular data (13.6%), and multimodal sources (8.4%); generation methods of prompting (72.9%), fine-tuning (22.0%) LLMs and specialized model (5.1%); and heterogeneous evaluations of intrinsic metrics (27.1%), human-in-the-loop assessments (55.9%), and LLM-based evaluations (13.6%). The analysis addresses current limitations in what, where, and how health professionals can leverage synthetic data generation for biomedical domains. Our review also highlights challenges in adaption across clinical domains, resource and model accessibility, and evaluation standardizations.