mlsanityData to 14 Oct 2026

Leveraging Generative AI Models for Synthetic Data Generation in Healthcare: Balancing Research and Privacy

arXiv 2305.05247 ↗First posted 9 May 2023cs.LG cs.AI cs.CRcited by 7

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Abstract (arXiv)

The widespread adoption of electronic health records and digital healthcare data has created a demand for data-driven insights to enhance patient outcomes, diagnostics, and treatments. However, using real patient data presents privacy and regulatory challenges, including compliance with HIPAA and GDPR. Synthetic data generation, using generative AI models like GANs and VAEs offers a promising solution to balance valuable data access and patient privacy protection. In this paper, we examine generative AI models for creating realistic, anonymized patient data for research and training, explore synthetic data applications in healthcare, and discuss its benefits, challenges, and future research directions. Synthetic data has the potential to revolutionize healthcare by providing anonymized patient data while preserving privacy and enabling versatile applications.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 13 of its 29 references that are in the map. First posted on arXiv 9 May 2023.

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13 of 29 references.

References · 13

  1. 2022Generative Adversarial Networkscited by 5,627
  2. 2013Auto-Encoding Variational Bayescited by 4,053
  3. 2016Deep Learning with Differential Privacycited by 2,728
  4. 2020The Future of Digital Health with Federated Learningcited by 633
  5. 2020Reliable Fidelity and Diversity Metrics for Generative Modelscited by 434
  6. 2018GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classificationcited by 242
  7. 2017Generating Multi-label Discrete Patient Records using Generative Adversarial Networkscited by 197
  8. 2021GANs for Medical Image Synthesis: An Empirical Studycited by 65
  9. 2019Generative adversarial networks (GAN) based efficient sampling of chemical space for inverse design of inorganic materialscited by 46
  10. 2022A Comprehensive Survey of Regression Based Loss Functions for Time Series Forecastingcited by 33
  11. 2020An Overview of Deep Learning Architectures in Few-Shot Learning Domaincited by 16
  12. 2021Fidelity and Privacy of Synthetic Medical Datacited by 16
1 more
  1. 2022Auto-labelling of Bug Report using Natural Language Processingcited by 4

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Cited by year

Citing papers per year: 2023: 4; 2024: 1; 2025: 2.

7 papers.

  1. 2023Advancements in Generative AI: A Comprehensive Review of GANs, GPT, Autoencoders, Diffusion Model, and Transformerscited by 36
  2. 2023A Comprehensive Survey of Evaluation Techniques for Recommendation Systemscited by 12
  3. 2024Synthetic Tabular Data Validation: A Divergence-Based Approachcited by 7
  4. 2025Enhancing Domain-Specific Retrieval-Augmented Generation: Synthetic Data Generation and Evaluation using Reasoning Modelscited by 4
  5. 2025Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inferencecited by 4
  6. 2023A Comparative Study of Text Embedding Models for Semantic Text Similarity in Bug Reportscited by 2
  7. 2023Generative Model-Driven Synthetic Training Image Generation: An Approach to Cognition in Rail Defect Detectioncited by 1