# Synthetic Data in Healthcare

arXiv: https://arxiv.org/abs/2304.03243 · HTML: https://mlsanity.com/paper/2304.03243/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 110 references that are in the map.
- First posted on arXiv 6 Apr 2023.

## Builds on (most cited first, 30 of 44)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Glow: Generative Flow with Invertible 1x1 Convolutions](https://mlsanity.com/paper/1807.03039/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images](https://mlsanity.com/paper/2003.09871/) (2020)
- [GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification](https://mlsanity.com/paper/1803.01229/) (2018)
- [Doctor AI: Predicting Clinical Events via Recurrent Neural Networks](https://mlsanity.com/paper/1511.05942/) (2015)
- [Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks](https://mlsanity.com/paper/1807.10225/) (2018)
- [Data augmentation using learned transformations for one-shot medical image segmentation](https://mlsanity.com/paper/1902.09383/) (2019)
- [Deep Learning for Medical Anomaly Detection -- A Survey](https://mlsanity.com/paper/2012.02364/) (2020)
- [DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks](https://mlsanity.com/paper/2110.12884/) (2021)
- [Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network](https://mlsanity.com/paper/1802.09655/) (2018)
- [DataPerf: Benchmarks for Data-Centric AI Development](https://mlsanity.com/paper/2207.10062/) (2022)
- [Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting](https://mlsanity.com/paper/1906.09531/) (2019)
- [A Review of Challenges and Opportunities in Machine Learning for Health](https://mlsanity.com/paper/1806.00388/) (2018)
- [CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection](https://mlsanity.com/paper/2103.05094/) (2021)
- [Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods](https://mlsanity.com/paper/2106.07153/) (2021)
- [Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training](https://mlsanity.com/paper/1711.06606/) (2017)
- [Camera Measurement of Physiological Vital Signs](https://mlsanity.com/paper/2111.11547/) (2021)
- [A Multifaceted Benchmarking of Synthetic Electronic Health Record Generation Models](https://mlsanity.com/paper/2208.01230/) (2022)
- [Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records](https://mlsanity.com/paper/1709.01648/) (2017)
- [DermGAN: Synthetic Generation of Clinical Skin Images with Pathology](https://mlsanity.com/paper/1911.08716/) (2019)
- [SimGANs: Simulator-Based Generative Adversarial Networks for ECG Synthesis to Improve Deep ECG Classification](https://mlsanity.com/paper/2006.15353/) (2020)

## Built on by (most cited first, 8 of 8)

- [A Unified Framework for Generative Data Augmentation: A Comprehensive Survey](https://mlsanity.com/paper/2310.00277/) (2023)
- [Generating Clinically Realistic EHR Data via a Hierarchy- and Semantics-Guided Transformer](https://mlsanity.com/paper/2502.20719/) (2025)
- [Beyond Real Data: Synthetic Data through the Lens of Regularization](https://mlsanity.com/paper/2510.08095/) (2025)
- [Should ChatGPT and Bard Share Revenue with Their Data Providers? A New Business Model for the AI Era](https://mlsanity.com/paper/2305.02555/) (2023)
- [F?D: On understanding the role of deep feature spaces on face generation evaluation](https://mlsanity.com/paper/2305.20048/) (2023)
- [Leveraging Generative AI Through Prompt Engineering and Rigorous Validation to Create Comprehensive Synthetic Datasets for AI Training in Healthcare](https://mlsanity.com/paper/2504.20921/) (2025)
- [From Statistical Fidelity to Clinical Consistency: Scalable Generation and Auditing of Synthetic Patient Trajectories](https://mlsanity.com/paper/2603.06720/) (2026)
- [Enhancing Facial Expression Recognition in Head-Mounted Displays with Synthetic Data](https://mlsanity.com/paper/2607.04490/) (2026)

## Abstract (arXiv)

Synthetic data are becoming a critical tool for building artificially intelligent systems. Simulators provide a way of generating data systematically and at scale. These data can then be used either exclusively, or in conjunction with real data, for training and testing systems. Synthetic data are particularly attractive in cases where the availability of ``real'' training examples might be a bottleneck. While the volume of data in healthcare is growing exponentially, creating datasets for novel tasks and/or that reflect a diverse set of conditions and causal relationships is not trivial. Furthermore, these data are highly sensitive and often patient specific. Recent research has begun to illustrate the potential for synthetic data in many areas of medicine, but no systematic review of the literature exists. In this paper, we present the cases for physical and statistical simulations for creating data and the proposed applications in healthcare and medicine. We discuss that while synthetics can promote privacy, equity, safety and continual and causal learning, they also run the risk of introducing flaws, blind spots and propagating or exaggerating biases.