mlsanityData to 14 Oct 2026

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

arXiv 2506.16594 ↗First posted 19 Jun 2025cs.CLcited by 3

On the map: Synthetic data and self-improvement for LLMs → Medical synthetic data and agents

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.

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.

Builds on

28 of 84 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Utilizing Large Language Models to Generate Synthetic Data to Increase the Performance of BERT-Based Neural Networks8 May

Other references · 27

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2024On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Surveycited by 244
  3. 2023A Study of Generative Large Language Model for Medical Research and Healthcarecited by 163
  4. 2023Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitationscited by 100
  5. 2021Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarizationcited by 76
  6. 2020End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systemscited by 65
  7. 2023Large Language Models to Identify Social Determinants of Health in Electronic Health Recordscited by 55
  8. 2023Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notescited by 51
  9. 2023NoteChat: A Dataset of Synthetic Doctor-Patient Conversations Conditioned on Clinical Notescited by 48
  10. 2024Improving Medical Reasoning through Retrieval and Self-Reflection with Retrieval-Augmented Large Language Modelscited by 34
  11. 2023Teaching Language Models to Hallucinate Less with Synthetic Taskscited by 31
  12. 2023Two Directions for Clinical Data Generation with Large Language Models: Data-to-Label and Label-to-Datacited by 26
15 more
  1. 2024TWIN-GPT: Digital Twins for Clinical Trials via Large Language Modelcited by 24
  2. 2022The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategiescited by 20
  3. 2023Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Modelscited by 20
  4. 2024Chain-of-Interaction: Enhancing Large Language Models for Psychiatric Behavior Understanding by Dyadic Contextscited by 19
  5. 2023Distilling Large Language Models for Matching Patients to Clinical Trialscited by 16
  6. 2024De-identification is not enough: a comparison between de-identified and synthetic clinical notescited by 14
  7. 2024SYNFAC-EDIT: Synthetic Imitation Edit Feedback for Factual Alignment in Clinical Summarizationcited by 12
  8. 2024Socially Aware Synthetic Data Generation for Suicidal Ideation Detection Using Large Language Modelscited by 10
  9. 2024A Review on Generative AI Models for Synthetic Medical Text, Time Series, and Longitudinal Datacited by 10
  10. 2024Real-Time Multimodal Cognitive Assistant for Emergency Medical Servicescited by 8
  11. 2022Textual Data Augmentation for Patient Outcomes Predictioncited by 6
  12. 2024Examining Imbalance Effects on Performance and Demographic Fairness of Clinical Language Modelscited by 4
  13. 2024Robust Privacy Amidst Innovation with Large Language Models Through a Critical Assessment of the Riskscited by 3
  14. 2024Synthetic Knowledge Ingestion: Towards Knowledge Refinement and Injection for Enhancing Large Language Modelscited by 3
  15. 2024DKE-Research at SemEval-2024 Task 2: Incorporating Data Augmentation with Generative Models and Biomedical Knowledge to Enhance Inference Robustnesscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Knowledge-driven Augmentation and Retrieval for Integrative Temporal Adaptationcited by 1
  2. 2026BLUEmed: Retrieval-Augmented Multi-Agent Debate for Clinical Error Detectioncited by 0
  3. 2026Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studiescited by 0

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  1. 2025SynLLM: A Comparative Analysis of Large Language Models for Medical Tabular Synthetic Data Generation via Prompt Engineering
  2. 2024Biomedical Large Languages Models Seem not to be Superior to Generalist Models on Unseen Medical Data
  3. 2024Can Large Language Models Replace Data Scientists in Biomedical Research?
  4. 2024Zero-shot and Few-shot Generation Strategies for Artificial Clinical Records
  5. 2024A Survey for Large Language Models in Biomedicine
  6. 2026Fidelity, Diversity, and Privacy: A Multi-Dimensional LLM Evaluation for Clinical Data Augmentation
  7. 2025FASTGEN: Fast and Cost-Effective Synthetic Tabular Data Generation with LLMs
  8. 2024A Review on Scientific Knowledge Extraction using Large Language Models in Biomedical Sciences