# A Scoping Review of Synthetic Data Generation by Language Models in Biomedical Research and Application: Data Utility and Quality Perspectives

arXiv: https://arxiv.org/abs/2506.16594 · HTML: https://mlsanity.com/paper/2506.16594/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Medical synthetic data and agents, one of 13 directions in Synthetic data and self-improvement for LLMs.
- Builds on 28 of its 84 references that are in the map.
- First posted on arXiv 19 Jun 2025.

## Placed in

- Synthetic data and self-improvement for LLMs / [Medical synthetic data and agents](https://mlsanity.com/synth/medical-synthetic-data-and-agents/)

## Builds on (most cited first, 28 of 28)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey](https://mlsanity.com/paper/2406.15126/) (2024)
- [A Study of Generative Large Language Model for Medical Research and Healthcare](https://mlsanity.com/paper/2305.13523/) (2023)
- [Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations](https://mlsanity.com/paper/2310.07849/) (2023)
- [Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization](https://mlsanity.com/paper/2110.07356/) (2021)
- [End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems](https://mlsanity.com/paper/2010.06028/) (2020)
- [Large Language Models to Identify Social Determinants of Health in Electronic Health Records](https://mlsanity.com/paper/2308.06354/) (2023)
- [Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes](https://mlsanity.com/paper/2309.00237/) (2023)
- [NoteChat: A Dataset of Synthetic Doctor-Patient Conversations Conditioned on Clinical Notes](https://mlsanity.com/paper/2310.15959/) (2023)
- [Improving Medical Reasoning through Retrieval and Self-Reflection with Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2401.15269/) (2024)
- [Teaching Language Models to Hallucinate Less with Synthetic Tasks](https://mlsanity.com/paper/2310.06827/) (2023)
- [Two Directions for Clinical Data Generation with Large Language Models: Data-to-Label and Label-to-Data](https://mlsanity.com/paper/2401.06774/) (2023)
- [TWIN-GPT: Digital Twins for Clinical Trials via Large Language Model](https://mlsanity.com/paper/2404.01273/) (2024)
- [The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies](https://mlsanity.com/paper/2212.08104/) (2022)
- [Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models](https://mlsanity.com/paper/2311.00287/) (2023)
- [Chain-of-Interaction: Enhancing Large Language Models for Psychiatric Behavior Understanding by Dyadic Contexts](https://mlsanity.com/paper/2403.13786/) (2024)
- [Distilling Large Language Models for Matching Patients to Clinical Trials](https://mlsanity.com/paper/2312.09958/) (2023)
- [De-identification is not enough: a comparison between de-identified and synthetic clinical notes](https://mlsanity.com/paper/2402.00179/) (2024)
- [SYNFAC-EDIT: Synthetic Imitation Edit Feedback for Factual Alignment in Clinical Summarization](https://mlsanity.com/paper/2402.13919/) (2024)
- [Socially Aware Synthetic Data Generation for Suicidal Ideation Detection Using Large Language Models](https://mlsanity.com/paper/2402.01712/) (2024)
- [A Review on Generative AI Models for Synthetic Medical Text, Time Series, and Longitudinal Data](https://mlsanity.com/paper/2411.12274/) (2024)
- [Real-Time Multimodal Cognitive Assistant for Emergency Medical Services](https://mlsanity.com/paper/2403.06734/) (2024)
- [Textual Data Augmentation for Patient Outcomes Prediction](https://mlsanity.com/paper/2211.06778/) (2022)
- [Examining Imbalance Effects on Performance and Demographic Fairness of Clinical Language Models](https://mlsanity.com/paper/2412.17803/) (2024)
- [Robust Privacy Amidst Innovation with Large Language Models Through a Critical Assessment of the Risks](https://mlsanity.com/paper/2407.16166/) (2024)
- [Synthetic Knowledge Ingestion: Towards Knowledge Refinement and Injection for Enhancing Large Language Models](https://mlsanity.com/paper/2410.09629/) (2024)
- [Utilizing Large Language Models to Generate Synthetic Data to Increase the Performance of BERT-Based Neural Networks](https://mlsanity.com/paper/2405.06695/) (2024)
- [DKE-Research at SemEval-2024 Task 2: Incorporating Data Augmentation with Generative Models and Biomedical Knowledge to Enhance Inference Robustness](https://mlsanity.com/paper/2404.09206/) (2024)

## Built on by (most cited first, 3 of 3)

- [Knowledge-driven Augmentation and Retrieval for Integrative Temporal Adaptation](https://mlsanity.com/paper/2604.22098/) (2026)
- [BLUEmed: Retrieval-Augmented Multi-Agent Debate for Clinical Error Detection](https://mlsanity.com/paper/2604.10389/) (2026)
- [Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies](https://mlsanity.com/paper/2608.05993/) (2026)

## Abstract (arXiv)

Synthetic data generation using large language models (LLMs) demonstrates substantial promise in addressing biomedical data challenges and shows increasing adoption in biomedical research. This study systematically reviews recent advances in synthetic data generation for biomedical applications and clinical research, focusing on how LLMs address data scarcity, utility, and quality issues with different modalities. We conducted a scoping review following PRISMA-ScR guidelines and searched literature published between 2020 and 2025 through PubMed, ACM, Web of Science, and Google Scholar. A total of 59 studies were included based on relevance to synthetic data generation in biomedical contexts. Among the reviewed studies, the predominant data modalities were unstructured texts (78.0%), tabular data (13.6%), and multimodal sources (8.4%). Common generation methods included LLM prompting (74.6%), fine-tuning (20.3%), and specialized models (5.1%). Evaluations were heterogeneous: intrinsic metrics (27.1%), human-in-the-loop assessments (44.1%), and LLM-based evaluations (13.6%). However, limitations and key barriers persist in data modalities, domain utility, resource and model accessibility, and standardized evaluation protocols. Future efforts may focus on developing standardized, transparent evaluation frameworks and expanding accessibility to support effective applications in biomedical research.