Synthetic Health Data: How Artificial Patient Records Are Accelerating Medical Research Without Compromising Privacy

AI-generated clinical datasets are giving researchers, pharmaceutical companies, and health systems a way to innovate faster while keeping real patient information locked away.

A pharmaceutical company needs health records from 500,000 patients to train a diagnostic AI model. A rare disease researcher can only find 47 documented cases globally. A hospital consortium wants to share clinical data across borders, but privacy regulations make the legal process take longer than the research itself.

Synthetic health data solves all three problems. By using AI to generate artificial patient records that preserve the statistical patterns of real clinical data without containing information from any actual person, synthetic health data is removing one of medicine’s most persistent bottlenecks: the gap between the data researchers need and the data privacy laws allow them to access.

The technology has moved well beyond proof of concept. Companies like MDClone, Syntegra, Synthia, and Syntho are generating synthetic datasets at scale for institutions including the National Institutes of Health, the Department of Veterans Affairs, and the Bill and Melinda Gates Foundation. Cedars-Sinai adopted a synthetic data platform in 2025 to power its AI and machine learning research. And the broader synthetic data generation market is projected to grow from roughly $350 million in 2023 to over $2 billion by the end of the decade, with healthcare as one of the fastest-growing segments.

This is what happens when a well-designed technology meets an urgent need.

What Is Synthetic Health Data and How Does It Work?

Synthetic health data consists of artificially generated patient records that statistically mirror real-world clinical information without containing data from any actual individual. The records look and behave like real patient data when analyzed. They contain diagnoses, lab results, medication histories, demographic details, and clinical outcomes. But no real person is behind any of them.

The generation process typically works in three stages. First, a machine learning model (often a generative adversarial network, or GAN, though variational autoencoders and diffusion models are also used) is trained on a real clinical dataset. The model learns the statistical relationships, distributions, and correlations within that data. Second, the model generates a new dataset that reproduces those patterns without copying any individual record. Third, the synthetic output is validated against the original data to confirm statistical fidelity and tested for re-identification risk to confirm privacy protection.

The result is a dataset that is, as the industry describes it, "realistic but not real." Researchers can analyze it, train AI models on it, and share it across institutions with far fewer regulatory constraints than real patient data requires.

Why Healthcare AI Needs Synthetic Data: The Privacy-Access Problem

Healthcare AI requires enormous volumes of clinical data to function properly. Training a diagnostic algorithm to detect early-stage cancer or predict hospital readmissions demands datasets that are large, diverse, and clinically detailed. But high-quality patient data has historically been one of the hardest resources in medicine to access.

Privacy regulations are the primary constraint. HIPAA in the United States and GDPR in Europe strictly control how patient information can be stored, shared, and used for research. These protections are essential. But they also create bureaucratic and technical barriers that can add months or years to research timelines. Contractual agreements, institutional review board approvals, and data governance frameworks all slow the process.

The available data also skews in problematic ways. Medical datasets are disproportionately drawn from acute care settings like ICUs, leaving chronic illness management, outpatient care, and diverse demographic populations underrepresented. An AI model trained on skewed data produces skewed predictions.

Synthetic health data addresses both problems at once. It preserves the statistical relationships researchers need while removing the identifying information that triggers regulatory restrictions. And it can be engineered to correct biases present in the original dataset, creating more balanced, representative training data for healthcare AI.

Synthetic Health Data Platforms: MDClone, Syntegra, Synthia, and Syntho

Several companies have built commercial platforms for generating synthetic health data at enterprise scale.

MDClone, founded in 2016 in Israel and now operating across the U.S., Canada, and Israel, provides a self-service data analytics platform powered by its patented Synthetic Data Engine. The company has raised over $100 million in funding and partners with the National Institutes of Health, the Department of Veterans Affairs, Intermountain Healthcare, and the Washington University School of Medicine. Its platform converts real electronic health record (EHR) data into synthetic versions that maintain statistical comparability, reducing data access timelines from months to days according to customer reports.

Syntegra, headquartered in San Mateo, California, generated synthetic versions of 2.6 billion rows of data from over 413,000 COVID-19 patients for the NIH’s National COVID Cohort Collaborative. That project expanded pandemic research access while protecting patient privacy. The Bill and Melinda Gates Foundation also contracted with Syntegra to produce synthetic versions of clinical trial data for HIV and maternal health programs.

Syntho, based in the Netherlands, has partnered with Cedars-Sinai to support the health system’s AI and machine learning research. Synthia, a European project backed by €12.43 million in EU funding, is developing validated tools and methods for synthetic data generation across healthcare applications.

On the open-source side, Synthea, developed by the MITRE Corporation, is the most widely used synthetic patient generator. It models complete medical histories for synthetic patients using publicly available health statistics and clinical guidelines, outputting data in HL7 FHIR, C-CDA, and CSV formats. The tool is free, unrestricted, and used globally for research, education, and software development.

How Synthetic Patient Data Is Used Across Healthcare

Drug development and clinical trials. Pharmaceutical companies use synthetic health data to accelerate drug development timelines. Synthetic cohorts help identify optimal trial endpoints before investing in actual patient recruitment. The data also supports real-world evidence studies by providing access to diverse, representative patient populations that would be difficult or impossible to assemble from a single institution’s records.

AI and diagnostic tool training. Synthetic datasets enable AI diagnostic models to train on broader, more representative data. This is especially valuable for rare conditions and diverse patient populations that individual health systems rarely see in sufficient numbers. Models trained on richer data produce more accurate predictions across a wider range of clinical scenarios.

Rare disease research. Researchers studying rare diseases often lack the patient numbers needed for statistically meaningful analysis. Synthetic health data allows them to generate additional records that match the statistical properties of their limited real-world cohorts, enabling hypothesis testing and algorithm development that would otherwise be impossible.

Cross-institutional collaboration. Health systems can share synthetic versions of their clinical data with partner organizations without the governance overhead that real patient data requires. This enables multi-site research collaborations and benchmarking that previously required lengthy legal review.

Education and training. Medical schools and health system training programs use synthetic datasets to give students and trainees hands-on experience with realistic clinical data. This avoids the privacy restrictions and data access delays that come with using real patient records for educational purposes.

Limitations of Synthetic Health Data: Model Collapse and Quality Risks

Synthetic health data is a powerful tool, but it has real limitations that users need to understand.

The most significant is a phenomenon called model collapse. Research published in Nature in 2024 by Shumailov et al. demonstrated that when AI models train exclusively on synthetic data through multiple iterations, quality degrades. The model progressively loses information from the tails of the original data distribution. Minority populations, rare conditions, and edge cases disappear first. After enough generations, the output drifts so far from reality that it becomes unusable.

This finding has shaped how the field approaches synthetic data. Leading researchers and institutions now treat it as a complement to real patient data, not a replacement. Synthetic records fill specific gaps: rare genetic syndromes, underrepresented demographics, clinical edge cases. Real patient data provides the statistical anchor.

Other limitations include data fidelity challenges. Not all data types are equally well suited to synthetic generation. Discrete genetic data, complex temporal relationships, and certain imaging modalities remain difficult to reproduce synthetically with sufficient accuracy. Validation is also an ongoing challenge. There is no universally accepted benchmark for confirming that a synthetic dataset is statistically faithful to its source while also being genuinely privacy-safe.

Governance and Validation: Building Trust in Synthetic Clinical Data

As synthetic health data moves from experimental to operational, the field is developing governance frameworks that balance innovation with accountability.

Leading vendors are investing in validation and privacy assessment infrastructure. Syntegra’s documentation references third-party privacy evaluation and statistical fidelity testing. MDClone’s platform undergoes continuous validation by the health systems using it, creating a feedback loop that improves data quality over time. Cedars-Sinai’s partnership with Syntho includes explicit recognition that synthetic data has limitations with certain data types and requires careful quality assessment.

Healthcare organizations adopting synthetic data are converging on several best practices: tagging datasets with metadata that documents source, generation method, and validation history; setting defined ratios between synthetic and real data in AI training sets; using automated monitoring tools to track alignment between synthetic outputs and real-world patterns; and conducting regular re-identification risk assessments.

Institutions report that combining synthetic data for rapid exploration with real data for validation offers both speed and reliability. The approach lets researchers test hypotheses quickly on synthetic records, then confirm findings against real patient data before drawing clinical conclusions.

Regulatory Landscape: FDA, GDPR, and the European Health Data Space

The regulatory framework for synthetic health data is evolving alongside the technology.

In the United States, the FDA has expressed growing interest in how synthetic and simulated data might inform regulatory decisions. The agency’s January 2025 draft guidance on AI-enabled medical devices outlined lifecycle management recommendations that touch on training data quality, though no specific framework for synthetic data has been published. In December 2025, the FDA updated its guidance on real-world evidence, removing the requirement that submissions include individually identifiable patient data for certain device types. While this guidance focuses on real-world (not synthetic) data, the policy direction signals increasing openness to privacy-preserving data approaches.

In Europe, the European Health Data Space (EHDS), which entered into force in March 2025, aims to create a unified market for health data across the EU. Synthetic data is positioned as a key enabler of the EHDS, allowing cross-border research collaboration while maintaining compliance with GDPR’s strict privacy requirements. The European Medicines Agency (EMA) has also referenced synthetic data in draft guidance on AI in the medicinal product lifecycle.

The regulatory picture remains incomplete. No drug or medical device has been approved using exclusively synthetic data as evidence. But the direction is clear: regulators are actively exploring how synthetic data fits into their frameworks, and guidance is becoming more specific each year.

The Future of Synthetic Health Data: 2026 and Beyond

Synthetic health data is solving a problem that has frustrated researchers for years: how to access the clinical information they need while keeping patient privacy protected.

The impact is already visible. Rare disease researchers who once struggled to find enough patients can now create synthetic cohorts to test hypotheses. Health systems across different countries are collaborating without the usual bureaucratic tangles. Startups are building healthcare AI products without spending months navigating data access approvals.

Several trends will shape the next phase. Generation methods will improve, producing higher-fidelity synthetic records for complex data types including imaging, genomics, and longitudinal patient histories. Regulatory frameworks will mature, giving institutions clearer guidance on what synthetic data can and cannot be used for in clinical and regulatory contexts. Validation standards will emerge as the field converges on agreed-upon benchmarks for statistical fidelity and privacy protection.

The market trajectory supports this. With synthetic data generation projected to grow at compound annual rates above 30% through the decade, investment in healthcare applications will continue to accelerate.

Synthetic health data works because it doesn’t try to replace real patient information. It fills gaps, speeds up exploration, and removes barriers to collaboration, while real-world data stays at the centre of clinical decision-making. For healthcare organizations evaluating this technology, the practical question is no longer whether synthetic data has a role but how to deploy it effectively, with the right validation, governance, and quality controls in place.

FAQ: Synthetic Health Data

What is synthetic health data?

Synthetic health data consists of artificially generated patient records that reproduce the statistical patterns and clinical relationships found in real medical datasets without containing information from any actual individual. These records can include diagnoses, lab results, medications, demographics, and outcomes. They are produced using machine learning models trained on real clinical data and are designed to be analytically useful while posing minimal re-identification risk.

Is synthetic health data HIPAA compliant?

Synthetic health data can significantly reduce HIPAA compliance concerns because it does not contain protected health information (PHI) from real patients. However, compliance depends on the entire data pipeline: the generation model, validation methods, storage infrastructure, and institutional policies all factor in. Organizations should conduct re-identification risk assessments and work with their compliance teams to evaluate each use case.

What is the difference between synthetic data and de-identified data?

De-identified data starts with real patient records and removes or obscures identifying details. Synthetic data is generated from scratch by a machine learning model trained on real data patterns. De-identified data always carries some residual re-identification risk because the underlying records are real. Synthetic data is designed to have no direct link to any individual patient, though the generation model’s exposure to real data means re-identification risk is not zero and should be assessed.

What is model collapse, and how does it affect synthetic data?

Model collapse occurs when AI models trained recursively on synthetic data progressively lose information from the tails of the original data distribution. Rare cases and minority populations disappear first, and after enough iterations, the model’s output becomes unreliable. Research published in Nature in 2024 documented this phenomenon. The practical response has been to use synthetic data strategically alongside real patient data, not as a wholesale replacement.

What are the leading synthetic health data platforms?

Major commercial platforms include MDClone (Israel/U.S., $100M+ raised, partners with NIH and VA), Syntegra (U.S., works with NIH and Gates Foundation), Syntho (Netherlands, partners with Cedars-Sinai), and Synthia (EU-funded project). On the open-source side, Synthea, developed by the MITRE Corporation, is the most widely used synthetic patient generator globally.

What Comes Next

Synthetic health data is one of those technologies that solves a genuine structural problem in healthcare. Researchers need clinical data. Privacy laws restrict access to it. Synthetic data creates a practical middle ground that lets both priorities coexist.

For health system leaders, the path forward involves piloting synthetic data platforms with defined use cases, establishing governance frameworks early, and investing in validation infrastructure that builds institutional confidence in the outputs. For researchers, it means treating synthetic data as a powerful accelerant for hypothesis generation and algorithm development, while maintaining real-world data as the standard for clinical conclusions.

The organizations moving on this now will have a meaningful head start as the technology matures and regulatory clarity improves.

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