The best synthetic data providers for healthcare and medical research in Europe include both commercial platforms and EU-funded research initiatives. European-headquartered commercial options include Syntho (Netherlands), MOSTLY AI (Austria), YData (Portugal), and Syntheticus (Switzerland), alongside bluegen.live. EU-funded research consortia such as SYNTHIA and SEARCH are building open, GDPR-aligned infrastructure for academic use. Choosing between them depends on your data types, regulatory requirements, and whether you need a production-ready platform or a research-grade tool. This article walks through the regulatory context, key selection criteria, the provider landscape, and practical considerations for European healthcare organizations.

Which regulations make synthetic data essential for European healthcare research?

Three overlapping regulatory frameworks make synthetic data a practical necessity for European healthcare research: the General Data Protection Regulation (GDPR), the European Health Data Space (EHDS) Regulation, and the EU AI Act. Together they create a compliance environment where sharing real patient data across institutions is legally complex, technically burdensome, and increasingly risky — and where synthetic data offers a viable path forward.

GDPR’s principle-based approach to data protection creates context-dependent barriers that are significantly harder to navigate than rule-based frameworks. Health data is classified as a special category of personal data under Article 9, meaning researchers need a valid legal basis, a Data Protection Impact Assessment, and often explicit consent before sharing records across institutions or borders. This complexity directly limits how much health data gets used for secondary research purposes.

The EHDS Regulation, which entered into force in March 2025, is the EU’s first sector-specific data space and explicitly addresses the secondary use of health data for research, innovation, and policy. Its framework requires that data access occur within secure processing environments where only aggregated or anonymous results can be extracted. Synthetic data fits naturally into this architecture. Most secondary use provisions apply from 2029, but organizations preparing now have a meaningful head start.

The EU AI Act classifies most health AI applications as high-risk, with comprehensive obligations becoming fully enforceable in August 2026. Its data governance requirements specifically address bias risks in AI systems, including requirements that models perform consistently across patient populations defined by age, ethnicity, or comorbidity. Synthetic data is a practical tool for filling training data gaps where real-world datasets are too sparse or unrepresentative to meet these standards.

It is worth noting that synthetic data does not automatically escape GDPR obligations. Creating synthetic data from real patient records is itself considered processing under GDPR, and whether the resulting dataset qualifies as truly anonymous depends on context. Organizations still need legal safeguards and, in many cases, a DPIA. Synthetic data reduces privacy risk substantially, but it does not eliminate regulatory responsibility.

What should you look for in a synthetic data provider for healthcare?

When evaluating synthetic data providers for healthcare, the four core dimensions are privacy guarantees, statistical fidelity, downstream utility, and GDPR compliance documentation. Providers that cannot demonstrate all four in a healthcare context should be approached with caution, regardless of their general market reputation.

Privacy guarantees and compliance architecture

Privacy guarantees are the most critical differentiator for European healthcare buyers. Look for providers that implement differential privacy, publish re-identification risk metrics, and offer privacy-by-design architectures rather than treating compliance as a post-generation audit step. Residual privacy risks exist even in well-generated synthetic data: models can inadvertently retain patterns from source data that create subtle re-identification pathways. Providers that combine synthetic generation with additional privacy-enhancing technologies offer stronger protection than those relying on generation alone.

Compliance documentation matters as much as technical architecture. A provider should be able to supply documentation that supports your own DPIA process, demonstrate alignment with the EHDS access framework requirements, and clearly explain how their outputs interact with GDPR’s definition of personal data in your specific deployment context.

Data modality coverage and technical depth

Healthcare data is not a single format. A credible provider for medical research needs to support electronic health records, clinical trial data, patient registries, medical imaging, genomics, longitudinal time series, and clinical notes. Providers that excel at tabular EHR generation may struggle with imaging or temporal data. Ask specifically which modalities the platform supports and request evidence from comparable use cases before committing.

Implementation complexity is also worth probing. Synthetic data generation typically requires more configuration and calibration than standard anonymization. Evaluate the provider’s implementation support capacity, realistic timelines for your data environment, and total cost of ownership over a multi-year horizon rather than focusing only on licensing fees.

Strategic fit and ecosystem integrations

Partnerships with EHR vendors, cloud platforms, and clinical research organizations are emerging as meaningful competitive differentiators. A provider already integrated with your existing infrastructure reduces implementation friction significantly. Also consider whether the provider offers on-premises deployment options, which matter considerably for hospital environments with strict data residency requirements.

What are the best synthetic data providers for healthcare in Europe?

The European synthetic data landscape for healthcare includes a mix of commercially available platforms and EU-funded research initiatives. The right choice depends on whether you need a production-ready commercial tool, an open research infrastructure, or a combination of both.

EU-funded research initiatives

SYNTHIA is the most ambitious EU-funded synthetic data initiative for healthcare, backed by the EU’s Innovative Health Initiative with a total project budget of around €22 million and running until 2029. It is a consortium of 39 partners focused on six disease areas including lung cancer, Alzheimer’s disease, and type 2 diabetes. SYNTHIA uses generative adversarial networks, federated learning, and hybrid modeling, and is developing a comprehensive evaluation framework for privacy, quality, and utility. It is a research platform rather than a commercial product, making it most relevant for academic and public health institutions.

SEARCH (Synthetic hEalthcare dAta goveRnanCe Hub) is a parallel IHI-funded consortium focused on cardiovascular, gastrointestinal, and gynecological diseases. It generates synthetic EHRs, genomics, imaging, and medical signal data across 26 institutions and is explicitly aligned with the EHDS framework.

European commercial providers

Syntho, based in Amsterdam, is one of the most documented European commercial providers in healthcare. It has worked with institutions including Lifelines biobank, Erasmus MC, and Cedars-Sinai Medical Center, and supports a broad range of data types including EHRs, clinical trials, patient registries, and longitudinal data. It won the 2023 Global SAS Hackathon in the Healthcare and Life Sciences category.

MOSTLY AI, headquartered in Vienna, raised significant venture funding and in early 2025 open-sourced an industry-grade SDK that allows hospitals to generate compliant synthetic datasets on-premises without vendor lock-in. This makes it an attractive option for organizations that want flexibility without a full in-house build.

YData, based in Portugal, was recognized as the most statistically accurate synthetic data generator in an independent 2025 benchmark. Syntheticus, based in Switzerland, focuses specifically on healthcare and pharma clients and has documented engagement with Roche’s digital innovation lab. Statice, originally from Germany, has been integrated into the Anonos platform.

It is also worth noting that Hazy, a UK-based pioneer in synthetic data, was acquired by SAS in late 2024 and its technology integrated into SAS Data Maker, giving enterprise SAS customers a built-in synthetic data capability.

How does synthetic data compare to anonymized data for medical research?

Synthetic data and anonymized data both aim to protect patient privacy while preserving research value, but they do so through fundamentally different mechanisms and involve different trade-offs. Neither approach is categorically superior; the right choice depends on your data type, research objective, and the privacy risk threshold your organization needs to meet.

Anonymization modifies or removes identifying fields from real patient records. It is well understood, has an established legal basis under GDPR, and preserves relational integrity across complex datasets. Its core weakness is that anonymizing high-dimensional data often degrades utility to the point of making datasets nearly unusable for research. As AI capabilities advance, previously anonymized datasets are also increasingly vulnerable to re-identification. Information like age, sex, and ethnicity can now be inferred from electrocardiograms and retinal photographs, and deep learning models can predict demographic attributes from medical images even after perturbation techniques have been applied.

Synthetic data, by contrast, generates entirely new records that statistically mirror the source population without being derived from any individual patient. This approach preserves statistical properties and supports downstream machine learning tasks more reliably than heavily anonymized data. A peer-reviewed study in npj Digital Medicine found that both synthetic and anonymized health insurance claims data produced results similar to original data, but both introduced higher uncertainty when estimating hazard ratios — a finding that underscores the importance of validation regardless of which approach you choose.

Synthetic data has meaningful limitations that are worth being honest about. Generators can struggle to maintain logical consistency across relational tables, for example producing a patient record where an appointment date follows a recorded death date. Hallucinations in tabular synthetic health data have also been shown to negatively affect prognostic machine learning models. And unlike anonymization, synthetic data has not yet been as thoroughly scrutinized for privacy attack vectors in peer-reviewed literature, meaning some risks are less well characterized.

For most European medical research applications, synthetic data offers a stronger utility-to-privacy balance than anonymization, particularly for training AI models and sharing data across institutions. But it should be treated as a complement to robust governance practices, not a replacement for them.

What types of healthcare data can synthetic data providers realistically generate?

Synthetic data providers can realistically generate tabular patient records, longitudinal EHR data, clinical trial datasets, medical imaging, genomics data, and clinical notes. The most mature and reliable capabilities are in structured tabular formats; imaging and genomics generation are advancing rapidly but involve greater technical complexity and more provider-to-provider variation.

Structured tabular data, including electronic health records, claims data, patient registries, and survey responses, represents the most established synthetic data modality. Providers across the market have demonstrated strong fidelity and utility for these formats, and the validation frameworks are well developed. Clinical trial data and longitudinal patient records add temporal complexity, but several providers now handle time series and irregularly sampled data with reasonable reliability.

Medical imaging synthesis has made significant strides, particularly in oncology, where generative models including GANs and VAEs have produced synthetic scans that support AI training without exposing real patient images. However, imaging generation is computationally intensive and the quality of outputs varies considerably by anatomy, modality, and the size of the source dataset used to train the generator.

Genomics and omics data present the greatest technical challenges. Discrete genetic data is a known limitation of current synthetic approaches, and providers themselves acknowledge this. Cedars-Sinai’s research team noted publicly that synthetic data does not handle all data types well, with discrete genetic data specifically called out. Organizations working with genomics should probe providers carefully on this modality before committing.

Clinical notes and unstructured text are an emerging frontier. Large language model-based frameworks have shown improvements in generating synthetic clinical narratives, but the hallucination risk in clinical text generation is a genuine concern that requires careful validation before these outputs are used in research pipelines.

A practical note: head-to-head benchmarks comparing specific providers across all modalities are not publicly available. When evaluating providers for a specific data type, request evidence from comparable healthcare use cases rather than relying on general capability claims.

How do you validate that synthetic healthcare data is fit for research?

Validating synthetic healthcare data requires assessing three dimensions: fidelity (how statistically similar the synthetic data is to the source), utility (how well models trained on synthetic data perform on real data), and privacy (the residual risk of re-identifying individuals from the synthetic output). A robust validation process addresses all three, not just one.

Fidelity and utility assessment

Fidelity evaluation compares the statistical properties of synthetic and real datasets, including distributions, correlations, and inter-variable relationships. Utility is best assessed using the Train-Synthetic, Test-Real (TSTR) paradigm, which involves training a machine learning model on synthetic data and testing it against held-out real data. This is the dominant benchmark for evaluating whether synthetic data retains the complex patterns needed for predictive tasks. A 2026 study in Advanced Science used TSTR to validate synthetic longitudinal records for a large diabetes cohort and successfully replicated clinical predictive performance, though deeper analysis revealed algorithmic limitations that would not have surfaced from surface-level metrics alone.

It is important to recognize that utility is always context-specific. Data that performs well for general trend analysis may yield incorrect results in specific statistical hypothesis tests. Validate against the actual downstream task your research requires, not a proxy task.

Privacy risk measurement

Privacy validation quantifies the risk that a synthetic record could be traced back to a real individual. Metrics including Identical Match Ratio, Distance to Closest Record, and Nearest Neighbour Distance Ratio are used to measure exact matches, near-matches, and outlier proximity, respectively. Providers should be able to supply these metrics as part of their standard output, and organizations should verify them independently rather than relying solely on vendor-generated reports.

Open-source evaluation tools including Synthetic Data Vault, Synthcity, and Table Evaluator provide independent assessment capabilities, though each uses its own nomenclature, which complicates direct comparison across providers. A consensus privacy metrics framework published in the journal Patterns in 2025 is a useful reference for organizations building a harmonized evaluation process.

One important gap to flag: no single EU-wide or EHDS-mandated validation standard for synthetic healthcare data has been finalized as of 2026. TEHDAS2 guidelines addressing synthetic data quality and privacy are still in consultation, with final guidance expected in the first half of 2026. Organizations should monitor these developments and build their validation processes to be adaptable as standards solidify.

When should a healthcare organization build synthetic data in-house versus using a provider?

Healthcare organizations should choose a commercial synthetic data provider when they need production-ready compliance, broad data modality support, and faster time to value. Building in-house makes sense when the organization has genuinely unique workflows that no commercial platform supports, or when synthetic data capability is a strategic differentiator that justifies a multi-year engineering investment.

The honest cost of building in-house is significant. A full-stack synthetic data capability in a healthcare context requires model engineering expertise, MLOps infrastructure, healthcare-specific compliance knowledge, and ongoing operational support. Industry estimates for comparable healthcare AI capabilities put annual investment requirements in the range of several million euros, and that figure does not account for the governance overhead of building your own privacy validation framework from scratch at a time when EU standards are still being finalized.

Commercial providers have already absorbed much of this overhead. They bring pre-built compliance documentation, established privacy metrics, support for multiple data modalities, and implementation experience from comparable deployments. For most European healthcare organizations, the buy or partner route delivers faster compliance and lower total cost of ownership than a greenfield build.

That said, there is a meaningful middle path. MOSTLY AI’s open-sourced SDK, released in early 2025, allows hospitals to run synthetic data generation on-premises without vendor dependency. This hybrid approach, combining a configurable open-source foundation with custom integration layers, suits organizations that want control over their data environment without taking on the full cost of proprietary model development.

One important caveat applies regardless of which path you choose: synthetic data should not be used as the primary training source for production clinical decision support systems or patient safety-critical applications. These scenarios require the full complexity and edge case representation that only real patient data provides. Synthetic data is a powerful tool for development, testing, and sharing, but it works best as a complement to real data governance rather than a replacement.

How bluegen.live supports synthetic data for European healthcare

For European healthcare organizations navigating GDPR, the EHDS framework, and the EU AI Act, finding a synthetic data platform that is purpose-built for the European regulatory environment makes a significant practical difference. bluegen.live is a Netherlands-based platform designed specifically for this context, offering concrete capabilities that address the most common barriers healthcare organizations face when working with sensitive patient data.

• GDPR-compliant by design: bluegen.live’s architecture is built around privacy-by-design principles, with differential privacy, re-identification risk metrics, and compliance documentation that directly supports your DPIA process.
• Complex relational data support: The platform handles many-to-many relationships and complex relational structures — a critical requirement for EHR data, patient registries, and longitudinal clinical datasets where referential integrity must be preserved.
• EHDS-ready infrastructure: bluegen.live is aligned with the emerging European Health Data Space framework, making it a forward-compatible choice as secondary use provisions come into force from 2029.
• On-premises deployment: For hospital environments with strict data residency requirements, bluegen.live supports on-premises deployment, keeping sensitive data within your controlled infrastructure.
• Regulated industry experience: bluegen.live works with Dutch and European enterprise clients across healthcare, finance, and energy, including public sector organizations, bringing implementation experience from comparable regulated environments.

If your organization is ready to explore what a GDPR-compliant synthetic data platform built for European healthcare looks like in practice, request a demo and see how bluegen.live can support your data strategy.