Executive Summary
The healthcare industry is undergoing a profound transformation driven by artificial intelligence (AI), yet its full potential is constrained by a fundamental paradox: the vast datasets required to train powerful AI models are the same datasets that must be rigorously protected to ensure patient privacy. This report provides an exhaustive analysis of synthetic data—artificially generated information that statistically mimics real-world patient data without containing any real patient records—as a paradigm-shifting solution to this challenge. By moving beyond traditional, subtractive privacy methods like anonymization, synthetic data generation offers a new framework for data access, enabling innovation while navigating the complex web of regulatory and ethical obligations.
This report begins by establishing a foundational understanding of synthetic data, providing a detailed taxonomy of its forms—fully synthetic, partially synthetic, and hybrid—and critically comparing its properties to legacy anonymization techniques. A deep technical dive follows, demystifying the core generative AI engines, primarily Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), that power modern data synthesis. The analysis details their distinct architectures, mechanisms, and suitability for different types of healthcare data, from medical imagery to structured electronic health records.
bundle-course—cloud-platform-professional-awsgcpazure By Uplatz
A comprehensive survey of transformative applications reveals the broad impact of synthetic data across the healthcare ecosystem. It is being used to train the next generation of diagnostic AI models, reimagine clinical trials through the creation of synthetic control arms, accelerate drug discovery by breaking down institutional data silos, and address the critical challenge of data scarcity in rare disease research. Case studies from leading institutions such as Cedars-Sinai, the GIMEMA consortium, and open-source projects like Synthea™ ground these applications in real-world practice.
However, synthetic data is not a panacea. The report critically examines the inherent trade-offs between data fidelity, utility, and privacy, outlining a validation framework of statistical, task-based, and privacy-risk metrics required to assess the quality and safety of synthetic datasets. A dedicated analysis explores the double-edged nature of algorithmic bias, detailing how synthetic data can be a powerful tool for promoting fairness by balancing datasets, but also a mechanism for amplifying inherited biases and introducing new ones if not governed carefully.
The intricate regulatory and ethical landscape is thoroughly navigated, covering compliance with frameworks like GDPR and HIPAA and the evolving guidance from bodies such as the FDA and EMA. The analysis extends beyond legal compliance to address profound ethical considerations, including the potential for group harms and the imperative to maintain public trust.
Finally, the report casts a forward-looking gaze on the future horizon, exploring the role of synthetic data in pioneering personalized medicine through the creation of “digital twins” and “virtual patients,” and its potential long-term impact on public health strategy. The report concludes with a set of strategic recommendations for key stakeholders—healthcare organizations, researchers, regulators, and technology developers—to foster the responsible and effective adoption of this transformative technology.
I. The Genesis of Synthetic Health Data: A New Paradigm for Privacy and Utility
The advancement of data-driven medicine, particularly in the realm of artificial intelligence, is predicated on access to vast, high-quality datasets. However, this necessity collides with a paramount ethical and legal obligation: the protection of patient privacy. For decades, the primary approach to resolving this tension involved techniques like anonymization and pseudonymization. These methods, however, create an inherent trade-off, where strengthening privacy often comes at the cost of data utility. Synthetic data has emerged as a fundamentally different approach, proposing not to alter or strip real data, but to generate entirely new, artificial data that preserves the statistical essence of the original without carrying the burden of individual identity.1 This represents a paradigm shift from a model of information removal to one of information replication, potentially re-writing the rules of data sharing and innovation in healthcare.
Defining Synthetic Data in a Clinical Context
At its core, synthetic data is artificially generated information that statistically mimics real-world patient data while containing no actual patient records.3 It is not merely “fake” or random data; it is the product of a sophisticated modeling process. As defined by the U.S. Census Bureau, it involves creating “microdata records by statistically modeling original data and then using those models to generate new data values that reproduce the original data’s statistical properties”.1
In a clinical setting, this can manifest in several ways. It could be an electronic health record (EHR) dataset where patient-identifiable information (PII) and other sensitive details are replaced with artificially generated values to prevent re-identification.1 It could also be a completely novel record, where all data points—from demographics to diagnoses and lab results—are synthesized to produce a wholly unreal patient profile that is nonetheless clinically plausible.1 The ultimate goal is to create fictional but functional datasets that capture the intricate patterns, correlations, and complexities of real patient-level data, allowing them to be analyzed as if they were the original.2
A Taxonomy of Synthesis
The term “synthetic data” encompasses a spectrum of methodologies, each offering a different balance between privacy protection and analytical value. The literature broadly classifies synthetic data into three categories, originally proposed by Aggarwal and Chu, and developed by pioneers like Rubin, Little, and Reiter.1
Fully Synthetic Data
First proposed by Donald Rubin in 1993, fully synthetic data contains no real data points whatsoever.1 The entire dataset is generated from a statistical model built upon the original data. This approach offers the highest level of privacy protection, as there is no direct link between a synthetic record and a real individual.1 This makes it an ideal choice when confidentiality is the primary concern, such as for public data releases or in environments where real data is completely inaccessible.5 However, this strong privacy guarantee can come at the cost of analytical value, or utility. The quality of the synthetic data is entirely dependent on the accuracy of the underlying statistical model; if the model fails to capture important nuances or complex relationships in the original data, those insights will be lost, potentially leading to lower analytic value.1
Partially Synthetic Data
In contrast to the all-or-nothing approach of full synthesis, partially synthetic data involves replacing only a subset of variables within the original dataset with synthetic values.1 Typically, the variables selected for replacement are those considered most sensitive or carrying the highest risk of disclosure.1 This method, first introduced by Roderick Little and formally named by Jerome Reiter, aims to strike a balance. By retaining a large portion of the original, real data, it preserves a high degree of data utility and realism.1 The U.S. Centers for Disease Control and Prevention (CDC) has used this approach to create public-use versions of datasets, replacing select variables that could lead to identification with synthetic values, allowing researchers to conduct analyses with high statistical accuracy while maintaining privacy protections.3 The primary drawback is the residual privacy risk; because the records still contain original values, the possibility of re-identification, though reduced, is not eliminated.1
Hybrid Synthetic Data
Hybrid synthetic data represents a more complex approach that combines elements of both real and synthetic data to form new records. In this method, for “each random record of real data, a close record in the synthetic data is chosen and then both are combined to form hybrid data”.1 This technique aims to achieve the best of both worlds: high data utility comparable to partially synthetic data, coupled with stronger privacy controls. However, this sophisticated blending process is more computationally intensive, requiring greater processing time and memory compared to the other two methods.1
Beyond Anonymization: A Critical Comparison
The advent of synthetic data is best understood in contrast to the traditional privacy-enhancing technologies (PETs) it seeks to improve upon, primarily anonymization and pseudonymization. While both aim to protect privacy, their fundamental mechanisms and resulting trade-offs are starkly different. This distinction is not merely technical; it represents a conceptual leap in how the conflict between data access and privacy is managed. Traditional methods operate on a principle of information removal or obfuscation, which inherently creates a direct trade-off: more privacy is achieved by sacrificing more data utility. Synthetic data, by contrast, operates on a principle of information replication without identity. It attempts to break the direct link in this privacy-utility curve, aiming to provide high utility and high privacy simultaneously.6
The process begins by recognizing that anonymization is a subtractive process. Techniques like data masking, suppression, encryption, or generalization involve removing or altering parts of the original dataset to obscure identifiers.7 This act of removal, however well-intentioned, inevitably damages the quality and integrity of the data. It can obscure meaningful patterns and break the subtle correlations that are essential for training sophisticated AI and machine learning models, thereby severely reducing the data’s utility.7 Furthermore, despite these sacrifices in utility, anonymized data remains vulnerable. Numerous studies have demonstrated that “anonymized” individuals can be re-identified by linking the dataset with other publicly available information, a persistent and growing risk.7
Synthetic data generation reframes this entire problem. It is an additive, or generative, process. It does not alter the original, sensitive dataset. Instead, it uses that dataset as a blueprint to train a generative model, which learns the underlying statistical distributions, patterns, and relationships within the data.1 Once trained, this model can generate an entirely new dataset of artificial records. Crucially, there is no one-to-one mapping between a synthetic record and a real patient record.2 The process is non-reversible; a synthetic patient cannot be traced back to a real individual.2 This makes the data “anonymous by design”.10 The core conflict is no longer “how much information must we remove to be safe?” but rather “how accurately can we model the information without modeling the individuals?”
This fundamental difference has profound implications across several key dimensions, as summarized in Table 1.
| Feature | Anonymized Data | Synthetic Data |
| Data Realism | Retains original data but with identifiers encrypted, suppressed, or destroyed, which damages data quality and structure.7 | Replicates real-world data with high accuracy (up to 99%), preserving the statistical properties and structure of the original data.7 |
| Privacy Risk | High risk of re-identification, as identifiers can be linked with external data or encryption keys can be compromised.7 | Considered 100% immune to re-identification risk as it contains no data from real individuals and cannot be traced back.7 |
| Data Utility for AI/ML | Low utility. Encryption and suppression reduce the data’s usability for training advanced machine learning models, affecting analysis accuracy.7 | High utility. Maintains and can even improve data quality through bias mitigation and rebalancing, enhancing data coverage for robust model training.7 |
| Regulatory Compliance | Regulated by data protection laws (e.g., GDPR, HIPAA) as the data originates from real individuals and must be protected accordingly.7 | Generally not regulated by data protection laws, as it is artificial data containing no personally identifiable information, simplifying sharing and collaboration.7 |
| Application Scope | Limited to specific, often small-scale scenarios where full data utility is not paramount. Unsuitable for most advanced AI/ML training.7 | Wide variety of use cases, including AI model training, rare scenario generation, data monetization, and secure data sharing across industries.7 |
| Scalability | Limited by the availability of real data and the cumbersome process of anonymization.12 | Easily scalable. Large volumes of data can be generated on demand once the generative model is trained.3 |
In summary, while anonymization has been a necessary tool, it is a legacy method fraught with compromises. It degrades the very data it seeks to protect and fails to provide a foolproof guarantee of privacy. Synthetic data offers a new path forward, one that promises to unlock the full potential of healthcare data for AI-driven innovation without forcing a direct and damaging trade-off with patient confidentiality.
II. The Engine Room: Generative AI Models for Data Synthesis
The transformative potential of synthetic data is realized through a class of powerful artificial intelligence models known as generative models. These algorithms are capable of learning the underlying patterns and structures within a real dataset and then generating new, artificial data that adheres to those learned rules. Within the healthcare domain, two types of deep learning architectures have become particularly prominent for their effectiveness in synthesizing complex medical data: Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). While both are capable of generating high-quality synthetic data, their internal mechanisms, strengths, and weaknesses are distinct, making their suitability dependent on the specific type of data and the intended application.
Generative Adversarial Networks (GANs): The Art of Deception
Introduced by Ian Goodfellow and his colleagues in 2014, Generative Adversarial Networks are built on a novel and intuitive concept: a competition between two neural networks.13 This adversarial architecture consists of a Generator and a Discriminator locked in a zero-sum game.14
Core Architecture and Mechanism
The process begins with the Generator. Its role is to create synthetic data. It starts by taking a random noise vector as input and attempts to transform it into a sample that resembles the real data (e.g., a synthetic patient record or a medical image).15
The Discriminator, in contrast, acts as a classifier. It is trained on a dataset of real examples and its job is to determine whether a given sample is authentic (from the real dataset) or fake (from the generator).14
The training process is a continuous feedback loop. The generator produces a batch of synthetic samples, which are then fed to the discriminator along with a batch of real samples. The discriminator provides a probability of authenticity for each sample. Initially, its job is easy, as the generator is only producing random noise.15 However, the generator receives feedback based on the discriminator’s performance—it learns from its failures. Using backpropagation, the generator adjusts its parameters to produce samples that are more likely to be classified as real by the discriminator.14 Over many iterations of this adversarial training, the generator becomes progressively better at creating realistic data, while the discriminator becomes more adept at spotting fakes. The process reaches an equilibrium when the generator’s outputs are so realistic that the discriminator’s success rate is no better than random chance (approximately 50%).15 At this point, the generator has successfully learned the underlying distribution of the real data.
Application in Healthcare
GANs have demonstrated remarkable success in generating high-fidelity, realistic medical images. They have been used to produce synthetic computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), retinal, and dermoscopic images that are often indistinguishable from real ones, even to trained experts.13 This makes them exceptionally valuable for augmenting datasets for training diagnostic AI. Beyond imaging, specialized GAN variants have been developed for other healthcare data types. Conditional GANs (CGANs) allow for more controlled generation by providing additional information (like a class label) to both the generator and discriminator. This enables the creation of targeted data, such as generating an MRI image specifically showing a tumor.5 For structured data like EHRs, Tabular GANs (TGANs) and Conditional Tabular GANs (CTGANs) have been designed to handle the mix of numerical and categorical variables found in patient records.5 For sequential data, models like TimeGANs can produce realistic time-series data, such as electrocardiograms (ECGs).5
Variational Autoencoders (VAEs): Probabilistic Generation
Variational Autoencoders, introduced by Kingma and Welling in 2013, offer a different, probabilistic approach to generative modeling.18 Instead of an adversarial competition, VAEs are based on an encoder-decoder architecture that learns a structured, low-dimensional representation of the data, known as the latent space.18
Core Architecture and Mechanism
A VAE consists of two main components: an Encoder and a Decoder.19
The Encoder network takes a high-dimensional input, such as a medical image or an EHR, and compresses it into a latent space representation. Unlike a standard autoencoder that maps the input to a single point in the latent space, a VAE’s encoder maps the input to a probability distribution—typically a normal distribution defined by a mean (
$$\mu$$
) and a variance (
$$\sigma^2$$
).18 This probabilistic encoding is a key feature, as it captures the inherent uncertainty and variability within the data.18
The Decoder network then performs the reverse process. It takes a point sampled from the latent space distribution and attempts to reconstruct the original high-dimensional input.18 The model is trained to simultaneously minimize two loss functions: a reconstruction loss (how well the decoder reconstructs the input) and a regularization term, the Kullback-Leibler (KL) divergence, which ensures that the learned latent space is well-organized and approximates a standard normal distribution.20
Once trained, the decoder can be used as a generative model. By sampling new points from the learned latent space distribution and passing them through the decoder, the VAE can generate novel data samples that are similar to, but not identical to, the original training data.18
Application in Healthcare
The characteristics of VAEs make them particularly well-suited for structured and sequential healthcare data. Their training process is more stable than that of GANs, and their organized, continuous latent space allows for the generation of diverse samples, which is crucial for representing the wide spectrum of patient profiles found in EHRs.20 VAEs have been used to generate synthetic longitudinal EHR sequences, enabling studies of disease progression over time.22 They are also employed for tasks such as data augmentation, dimensionality reduction, and even improving the quality of medical images through noise reduction in MRIs or artifact correction.18 Advanced variants like the causal recurrent VAE (CR-VAE) have been developed to learn and incorporate underlying causal relationships from multivariate time-series data, which is highly relevant for understanding complex biological processes.24
A Comparative Look at Generative Architectures
The decision to use a GAN versus a VAE is not merely a technical preference but a strategic choice driven by the specific requirements of the healthcare application. Their architectural differences lead to distinct trade-offs in performance, realism, and diversity. The adversarial objective of GANs—to make synthetic data indistinguishable from real data—drives the generator toward photorealism. This makes GANs the superior choice for tasks where visual fidelity is paramount, such as generating synthetic chest X-rays to train a diagnostic AI for radiology.13 However, this intense competition can be unstable and may lead to “mode collapse,” a common failure mode where the generator discovers a few highly realistic examples that consistently fool the discriminator and ceases to explore the full diversity of the data distribution, resulting in a lack of variety in the generated samples.20
Conversely, the objective of VAEs is to learn an efficient, probabilistic representation of the data and then reconstruct it.18 The KL divergence regularization term forces the latent space to be continuous and well-organized, which allows for smooth interpolation between data points and robust sampling of the entire data distribution.20 This makes VAEs excellent at generating a wide variety of plausible data, even if individual samples are sometimes perceived as less sharp or “blurrier” than those from a GAN.20 This high diversity is critical for applications involving structured data like EHRs, where the primary goal is to capture the full spectrum of patient scenarios and edge cases to test a clinical decision support system, rather than achieving perfect realism in any single record.
To leverage the strengths of both, hybrid models such as the VAE-GAN have been developed, which combine the architectures to generate high-dimensional data with both good diversity and high fidelity.5 The key characteristics of these two primary generative models are summarized in Table 2.
| Aspect | Generative Adversarial Networks (GANs) | Variational Autoencoders (VAEs) |
| Core Mechanism | An adversarial competition between a Generator (creates data) and a Discriminator (evaluates data).14 | An Encoder-Decoder architecture that learns a probabilistic, low-dimensional latent space representation of the data.18 |
| Training Stability | Challenging and parameter-sensitive. Can be unstable and difficult to converge due to the min-max game.20 | Generally easier and more stable to train with a well-defined loss function that converges reliably.20 |
| Realism/Fidelity | Typically produces higher-fidelity, sharper, and more realistic samples, especially for images.20 | Can produce less realistic or “blurrier” images, though excels in capturing the overall data distribution.20 |
| Sample Diversity | Prone to “mode collapse,” where the generator produces a limited variety of samples.20 | Robust diversity due to the structured, continuous nature of the latent space, making it less prone to mode collapse.20 |
| Latent Space Interpretability | The latent space is implicit and generally not interpretable, making controlled generation difficult.20 | The latent space is explicit, continuous, and interpretable, allowing for meaningful manipulation and interpolation.20 |
| Primary Healthcare Use Cases | Medical imaging (X-ray, CT, MRI), where high visual fidelity is critical. Data augmentation for diagnostic models.13 | Structured data (EHRs), time-series data, and applications where diversity and interpretability are essential, such as scenario testing.20 |
III. Transformative Applications Across the Healthcare Ecosystem
Synthetic data is rapidly moving from a theoretical concept to a practical tool with a wide array of applications across the healthcare landscape. Its core value proposition—providing realistic, privacy-preserving data at scale—addresses critical bottlenecks in research, development, and clinical practice. Across these diverse applications, a common theme emerges: synthetic data functions as a multi-purpose “research accelerator.” It is not primarily intended to replace real data for final, high-stakes clinical decision-making. Instead, its principal function is to accelerate every preceding step of the innovation lifecycle. It accelerates hypothesis testing by circumventing lengthy ethics board approvals 2, accelerates AI model development by providing abundant training data 28, accelerates clinical trials by simulating control arms 29, and accelerates public health modeling by enabling safe, large-scale simulations.30 It serves as a high-fidelity, low-risk proxy that allows the vast majority of foundational work to be completed efficiently, reserving the use of precious, highly regulated real data for the final validation stage.
Powering the Next Generation of Diagnostic AI
The development of robust AI-powered diagnostic tools is one of the most promising frontiers in medicine, but it is also one of the most data-hungry. Deep learning models require vast, diverse, and meticulously annotated datasets to achieve high levels of accuracy, a resource that is often scarce in healthcare due to privacy constraints, the high cost of annotation, and the simple rarity of certain diseases.31
Synthetic data generation provides a powerful solution through data augmentation.31 Generative models like GANs and VAEs can learn the characteristics of an existing, smaller dataset of medical images—such as MRIs, CT scans, or X-rays—and generate a multitude of new, synthetic images. This allows developers to dramatically expand their training sets, particularly for rare pathologies where real-world examples are few and far between.5 Beyond simply increasing volume, synthetic data can be engineered to improve the robustness and generalizability of AI models. By generating a wide array of clinical scenarios, including edge cases and variations that may be absent from the original limited dataset, developers can train their models to perform reliably across a broader range of real-world conditions.3
Reimagining Clinical Trials
Clinical trials are the cornerstone of evidence-based medicine, but they are notoriously slow, expensive, and ethically complex. Synthetic data offers several avenues to streamline and improve this critical process.
Before a trial even begins, researchers can use synthetic datasets for simulation and design. This allows them to model potential trial outcomes based on historical data patterns, test and refine statistical analysis approaches, and optimize cohort selection criteria, all without enrolling a single patient.3
Perhaps the most revolutionary application is the creation of synthetic control arms (SCAs), also known as “in-silico” or “virtual” control groups.15 In many trials, a portion of participants must be assigned to a placebo or standard-of-care group to provide a baseline for comparison. Generating a synthetic cohort that statistically mirrors the characteristics and expected outcomes of a real control group can reduce or even eliminate the need for a real-life placebo arm.15 This has profound benefits: it can lower trial costs, accelerate timelines, and allow for a larger number of patients to be enrolled in the active treatment arm.15 This approach is especially valuable in rare disease trials where recruiting a sufficient number of patients is a major challenge, or in oncology trials where assigning a terminally ill patient to a placebo can be ethically fraught.15
A landmark example of this is the GIMEMA AML1310 trial. Researchers in Italy used data from a real clinical trial for acute myeloid leukemia (AML) to generate a synthetic cohort. This virtual group’s outcomes, including complete remission rates and overall survival curves, were found to be perfectly consistent with those of the actual control group. This successful demonstration validates the feasibility of using SCAs in complex onco-hematological research, paving the way for more efficient and ethical trial designs in the future.29
Accelerating Medical Research and Drug Discovery
The traditional model of medical research is often hampered by data access barriers. Strict privacy regulations like HIPAA and GDPR, coupled with institutional data-sharing policies and lengthy Institutional Review Board (IRB) or ethics committee approval processes, create significant delays, often turning what should be weeks of work into months or even years.37
Synthetic data helps to dismantle these barriers. Because fully synthetic data contains no real patient information and is not considered data from human subjects, it can often bypass the rigorous IRB approval process required for real data.2 This dramatically reduces the “time-to-insight,” allowing researchers to quickly access data, test initial hypotheses, check project feasibility, and develop analytical code while waiting for approval to use the real dataset.2 This acceleration enables a more agile and iterative research cycle.
Leading academic medical centers are already embracing this model. Cedars-Sinai, for example, has adopted a synthetic data platform from the company Syntho to provide its researchers and students with rapid, on-demand access to realistic clinical data. This initiative, part of their broader Digital Innovation Platform, allows investigators to conduct studies and test theories without the typical bureaucratic hurdles, thereby accelerating the pace of clinical innovation.40
In drug discovery, synthetic data is enabling the creation of “digital twins” or “virtual patients.” These are dynamic computational models of individuals that can be used to simulate disease progression and predict responses to novel therapies, a cornerstone of personalized medicine.33 Researchers at Stanford University have demonstrated this potential by using generative AI to design dozens of novel antibiotic candidates and to synthesize virtual biopsy slides for inoperable brainstem cancers, allowing them to test drug efficacy in silico.41
Addressing the Unseen: Augmenting Datasets for Rare Diseases
Research into rare diseases is chronically stymied by its defining characteristic: a lack of patients, and therefore, a lack of data.42 This data scarcity makes it nearly impossible to conduct statistically significant studies or train effective AI models.
Synthetic data generation offers a powerful solution to this fundamental problem. Generative models can learn the complex patterns from the few available patient records and then generate a much larger, statistically consistent cohort of synthetic patients.45 This augmented dataset can provide the statistical power needed to identify patterns, test hypotheses, and train predictive models that would otherwise be unfeasible.5 For example, the RD-Connect GPAP initiative has created a public synthetic dataset for rare disease research. It was built by taking a public human genomic background and computationally inserting real, known disease-causing variants. This allows developers and researchers to test their analytical tools and methods on a realistic dataset without navigating the ethical and legal complexities of using real rare disease patient data.47
Fortifying Public Health and Health IT
The applications of synthetic data extend beyond individual patient care to the broader domains of public health and health information technology.
For epidemiology and public health policy, large-scale synthetic population datasets are invaluable tools for simulation. For instance, the US Synthetic Household Population dataset, which contains records for 300 million fictitious individuals with realistic sociodemographic and geographic attributes, has been used to model the spread of infectious diseases like influenza, assess the potential impact of public health interventions like school closures or targeted vaccination campaigns, and plan for disaster response.30 These simulations allow policymakers to test the effectiveness of different strategies in a safe, virtual environment before implementing them in the real world.34
In health IT, the development and testing of software, such as EHR systems and mobile health applications, requires access to realistic patient data to ensure functionality, scalability, and interoperability. Using real Protected Health Information (PHI) for these purposes is a significant compliance risk.3 Synthetic data provides a privacy-compliant alternative, enabling development teams to test EHR integrations, validate application performance across diverse patient scenarios, and implement continuous integration/continuous deployment (CI/CD) pipelines with realistic but artificial data.3 A prominent tool in this space is Synthea™, an open-source synthetic patient generator developed by The MITRE Corporation. It can produce detailed, longitudinal patient histories and output them in standard formats like FHIR (Fast Healthcare Interoperability Resources). This has made it a go-to resource for developers testing new health IT applications and for researchers creating synthetic data modules for specific conditions like sepsis, spina bifida, and opioid use disorder to support patient-centered outcomes research.30
IV. Navigating the Trilemma: Fidelity, Utility, and Privacy
While synthetic data offers a compelling solution to the data access problem in healthcare, its generation and application are governed by a complex and delicate balancing act. This is often referred to as the “trilemma,” a fundamental tension between three critical properties: Fidelity, Utility, and Privacy.52 Fidelity refers to the statistical similarity of the synthetic data to the original real data. Utility measures how well the synthetic data performs for a specific downstream task. Privacy is the guarantee that sensitive information about real individuals is not disclosed. These three goals are often in conflict; increasing one may come at the expense of another. Consequently, the validation of synthetic data is not a simple pass/fail test but a nuanced, multi-faceted assessment to determine if a dataset is “fit for purpose”.54 This evaluation process is highly context-dependent and, as of yet, lacks a universal “gold standard” methodology, presenting a significant governance challenge for organizations.55
The choice of metrics and the acceptable thresholds for each dimension of the trilemma are not absolute; they are contingent on the specific use case. Data intended for early-stage software testing, for example, may prioritize scalability and structural correctness over perfect statistical fidelity. In contrast, data generated to create a synthetic control arm for a regulatory submission would require the highest possible levels of fidelity and utility, even if it necessitates more complex privacy assessments. A review of 73 studies on the topic found no consensus on optimal evaluation methods, highlighting the fragmented landscape.55 This means that organizations cannot simply “validate” their synthetic data in a generic sense; they must validate it against the specific requirements of its intended application, creating a bespoke validation strategy for each use case.
Quantifying Quality: Validation Metrics and Frameworks
To navigate this trilemma, a robust validation framework is required, incorporating metrics that assess each dimension of data quality. These metrics can be broadly categorized as follows:
Fidelity Metrics (Statistical Similarity)
These “look-alike” metrics aim to answer the question: “How closely does the synthetic data’s statistical profile match the real data?”.56 They provide a general assessment of the quality of the generative model. Common techniques include:
- Distributional Comparisons: This involves comparing the marginal distributions of individual variables (e.g., the distribution of age) and the joint distributions of multiple variables. Statistical tests like the Kolmogorov-Smirnov (KS) test for continuous variables or divergence measures such as Jensen-Shannon Divergence and Kullback-Leibler (KL) Divergence are used to quantify the difference between the real and synthetic distributions.56
- Correlation Analysis: A correlation matrix of the synthetic data is computed and compared to that of the real data to ensure that the relationships and dependencies between variables have been preserved.56
- Composite Fidelity Scores: Some frameworks combine multiple statistical tests into a single, composite score to provide a holistic measure of fidelity. Examples include the Column-wise Statistical Fidelity (CSF) and General Statistical Fidelity (GSF) scores, which average the results of various similarity tests across features.57
Utility Metrics (Task-Based Performance)
These “work-alike” metrics move beyond general statistical properties to evaluate the data’s performance on a specific, practical task. They answer the question: “Is the synthetic data useful for my intended purpose?”.56
- Train on Synthetic, Test on Real (TSTR): This is the most widely accepted method for assessing utility in the context of machine learning.56 A predictive model (e.g., a logistic regression or a neural network) is trained exclusively on the synthetic dataset. Its performance is then evaluated on a held-out set of real data. This performance (measured by metrics like Area Under the Curve (AUC), accuracy, or F1-score) is compared to that of a baseline model trained on the real data. A small drop in performance for the synthetically-trained model indicates high utility.57
- Replication of Analytical Results: This is the ultimate test of utility. It involves conducting an entire research analysis (e.g., a survival analysis or a regression model) on both the real and synthetic datasets and comparing the conclusions. If the synthetic data leads to the same findings, effect sizes, and confidence intervals as the real data, it is considered to have very high utility.36
Privacy Metrics (Risk Assessment)
These metrics are designed to quantify the risk that the synthetic dataset could leak information about the individuals in the original training data. They answer the question: “How safe is this data?”.52
- Membership Inference Attacks (MIA): This is an adversarial test where an attacker attempts to determine whether a specific individual’s record was part of the original dataset used to train the generative model. A high success rate for the attacker indicates a significant privacy leak.52
- Attribute Inference: This measures the risk that an adversary, knowing some information about a real individual in the training set, could use the synthetic data to infer other sensitive attributes about that individual.52
- Distance-Based Metrics: These metrics assess how close synthetic records are to real records. The Nearest Neighbor Distance Ratio (NNDR), for example, compares the distance of each synthetic point to its nearest real neighbor and its second-nearest real neighbor. This helps ensure that the synthetic records are not simply verbatim copies or near-copies of real records, which would pose a major privacy risk.57 An exact match incidence of 0% is a fundamental requirement.57
Table 3 provides a consolidated framework of these key validation metrics.
| Category | Metric | What It Measures | Example Target/Threshold |
| Fidelity | Distributional Tests (e.g., KS test, MMD) | Statistical similarity between the distributions of variables in real vs. synthetic data. | Non-significant p-values (e.g., $p > 0.05$) on key variables.57 |
| Correlation Matrix Difference | The difference in correlation structures between variables in the two datasets. | Low mean absolute difference in correlation coefficients. | |
| Composite Fidelity Scores (CSF/GSF) | An aggregated score of statistical similarity across multiple univariate and bivariate tests. | High fidelity score (e.g., $\geq 85\%$).57 | |
| Utility | Train on Synthetic, Test on Real (TSTR) | The performance (e.g., AUC) of a model trained on synthetic data when evaluated on real data, compared to a model trained on real data. | Minimal drop in performance (e.g., AUC_synthetic $\approx$ AUC_real).57 |
| Replication of Analysis | Whether the conclusions of a specific clinical study (e.g., survival analysis) are the same when run on synthetic vs. real data. | The synthetic analysis yields the same clinical conclusions as the real analysis.57 | |
| Privacy | Membership Inference Attack (MIA) Risk | The likelihood that an attacker can determine if an individual’s record was in the training data. | The MIA classifier’s accuracy should be close to random chance (50%). |
| Nearest-Neighbor Distance Ratio (NNDR) | The proximity of synthetic records to real records, to detect copies or near-copies. | A ratio between 0.6 and 0.85 is often recommended to ensure points are not too dissimilar or too identical.57 | |
| Exact Match Incidence | The percentage of synthetic records that are identical to any record in the original dataset. | Must be 0%. No exact duplicates are permissible for privacy.57 |
The Impact of Privacy-Enhancing Technologies (PETs)
To bolster privacy guarantees, synthetic data generation can be combined with other PETs, most notably Differential Privacy (DP). DP is a rigorous mathematical framework that provides a provable guarantee of privacy by injecting carefully calibrated statistical noise into the training process of the generative model (e.g., creating a Differentially Private GAN, or DPGAN).10 This ensures that the output of the model is statistically indistinguishable whether or not any single individual’s data was included in the training set, thus limiting what can be inferred about any specific person.10
However, this formal privacy guarantee comes at a significant cost. Multiple studies have shown that enforcing DP during synthetic data generation can have a severely detrimental effect on both data fidelity and utility.61 The injected noise often disrupts and flattens the complex correlation structures and subtle patterns within the data. One systematic evaluation found that DP-enforced models significantly disrupted feature correlations, whereas non-DP synthetic models maintained good fidelity and utility without showing strong evidence of privacy breaches.61 This highlights the sharpest point of the trilemma: achieving provable privacy often renders the data analytically useless. This suggests that for many use cases, a risk-based approach using non-DP synthetic data combined with rigorous privacy metric evaluations may offer a more practical balance than the absolute guarantees of differential privacy.
V. The Double-Edged Sword: Algorithmic Bias in Synthetic Data
One of the most compelling promises of synthetic data in healthcare is its potential to address algorithmic bias and promote fairness in AI systems. Real-world medical datasets are often a reflection of historical and systemic inequities, containing underrepresented demographic groups, skewed samples, and biased patterns of care.63 When AI models are trained on such data, they not only learn these biases but can amplify them, leading to systems that perform poorly for minority groups and perpetuate health disparities.64 Synthetic data is presented as both a powerful tool to mitigate this problem and, paradoxically, a potential mechanism for its exacerbation.
The common understanding is that if the source data is biased, the synthetic data will be as well. However, the reality is more complex. The generative model itself acts as a second, independent source of bias. Generative models like GANs can both amplify the biases they inherit from the training data (spurious correlations) and introduce entirely new biases through their own mechanics (representation bias via mode collapse).66 This means that simply cleaning or re-weighting the input data is an insufficient strategy for ensuring fairness. A comprehensive approach must also govern and constrain the generative process itself to prevent the synthetic output from becoming even more biased than the original.
A Tool for Fairness: Mitigating Bias with Synthetic Data
The primary mechanism by which synthetic data can promote fairness is through the deliberate creation of balanced and representative datasets.3 Since the data generation process is controllable, developers can use it to correct for the imbalances found in real-world data.
This is typically achieved through oversampling of minority or underrepresented groups. If a dataset has a disproportionately small number of samples from a particular demographic (e.g., a specific race, gender, or age group), generative models can be trained on the data from that subgroup to produce additional, statistically similar synthetic samples.46 This process, often called data balancing or augmentation, results in a new, larger training set where all groups are more equally represented.63
Techniques like the Synthetic Minority Over-sampling Technique (SMOTE) and its variants, as well as more advanced deep learning models like GANs and VAEs, are used for this purpose.63 By training AI models on these more inclusive datasets, healthcare organizations can develop more equitable and effective decision-support tools that perform well across diverse populations, thereby mitigating the risk of disparities in healthcare outcomes.46 Studies have shown that this approach can significantly improve fairness metrics, such as Equal Opportunity, and reduce the number of false negatives for minority classes.69
The Risk of Amplification: How Generative Models Can Worsen Bias
Despite its potential as a fairness tool, the very nature of generative modeling can also lead to the amplification of existing biases and the introduction of new ones. This is a critical and often overlooked risk.
Learning Spurious Correlations (Correlation Bias)
Generative models are designed to be powerful pattern recognizers. They learn and replicate all the statistical relationships present in the training data, including those that are spurious or undesirable.66 If a real-world dataset contains a “malignant feature correlation”—for example, if a particular demographic group is associated with higher healthcare costs for socioeconomic rather than clinical reasons—a GAN will learn this association. In its effort to produce realistic data that can fool the discriminator, the generator may even strengthen this spurious correlation, as it represents a strong signal in the training data.67 The resulting synthetic data would therefore be even more biased than the original, and an AI model trained on it would be more likely to make discriminatory predictions.
Failure to Represent Subgroups (Representation Bias)
A second, more insidious form of bias is introduced by the generative model itself. GANs, due to the instability of their adversarial training process, are susceptible to a failure mode known as mode collapse.20 This occurs when the generator finds a limited number of “safe” examples that are effective at fooling the discriminator and then overproduces these samples, failing to learn the full diversity of the original data distribution. This disproportionately affects minority subgroups. The model may struggle to learn the patterns of small, underrepresented groups, leading it to under-sample or even completely ignore them in the generated dataset.66 This effectively erases these populations from the synthetic data, resulting in what is termed representation bias. An AI model trained on such data would have no knowledge of these subgroups and would almost certainly perform poorly when encountering them in a real-world clinical setting.66
Strategies for Bias Mitigation in Synthetic Data Generation
Recognizing that bias can be both inherited and introduced, researchers have developed a multi-stage approach to creating fair synthetic data. These strategies can be categorized into three types 65:
- Pre-processing Techniques: These methods involve modifying the original dataset before it is used to train the generative model. This can include re-weighting samples to give more importance to underrepresented groups, or sampling techniques to create a more balanced initial dataset. The goal is to remove or reduce discriminatory patterns at the source.65
- In-processing Techniques: These more advanced techniques modify the learning algorithm of the generative model itself to actively promote fairness during training. For example, the Bias-transforming GAN (Bt-GAN) framework introduces a fairness penalty into the generator’s loss function to discourage it from learning spurious correlations. Simultaneously, it uses a technique called score-based weighted sampling, which forces the generator to pay more attention to and learn from the underrepresented regions of the data manifold, directly combating representation bias.66
- Post-processing Techniques: These methods are applied to the output of the generative model. This can involve modifying the generated synthetic data or the predictions of a downstream model to ensure fair outcomes across different groups.65 For instance, after using a weighted sampling technique that might over-correct for representation bias, a technique like discriminator rejection sampling can be used to refine the final synthetic dataset and correct for any new biases that were introduced.66
By employing a combination of these strategies, it is possible to guide the synthetic data generation process not just toward realism, but also toward fairness, creating datasets that are not only privacy-preserving and useful but also equitable.
VI. The Regulatory and Ethical Gauntlet
The transition from real to synthetic data in healthcare does more than solve technical challenges; it fundamentally reshapes the regulatory and ethical landscape. While fully synthetic data can circumvent many of the privacy constraints that govern real patient information, it is not a “regulatory or ethical panacea”.11 Its use introduces a new set of complex questions related to governance, accountability, and the potential for harm. The central ethical concern shifts away from the traditional focus on individual consent and the protection of personally identifiable information. With synthetic data, the primary ethical and regulatory frontier becomes the downstream accountability for the decisions and impacts of the AI systems trained on it. The burden shifts from the data controller, tasked with protecting PII, to the model developer and deployer, who must ensure the fairness, safety, and validity of the tools they build.11
Navigating Global Data Protection Laws
The legal status of synthetic data is a critical and evolving area of debate, with different interpretations across major regulatory frameworks.
GDPR and the UK Data Protection Act
Under the General Data Protection Regulation (GDPR), the central question is whether fully synthetic data qualifies as “personal data”.71 According to Recital 26 of the GDPR, the principles of data protection do not apply to information that has been rendered anonymous in such a way that the data subject is “not or no longer identifiable”.71 However, the threshold for true anonymization is high. The determination rests on a contextual risk assessment of whether an individual could be identified by any “means reasonably likely to be used”.71
European data protection authorities have adopted a cautious stance, generally operating under the presumption that if the source data used to create the synthetic data was personal, then the synthetic output remains personal data unless it can be demonstrated with a high degree of confidence that re-identification risks are minimal.71 Critically, the very act of creating synthetic data from a real, personal dataset is itself a form of data processing and is therefore fully subject to GDPR rules.60
The UK’s Information Commissioner’s Office (ICO) has provided draft guidance suggesting a path forward. It indicates that synthetic data generated with robust privacy-enhancing technologies like differential privacy can potentially meet the criteria for anonymous data by reducing the identifiability risk to a “sufficiently remote level”.60
HIPAA (USA)
In the United States, the Health Insurance Portability and Accountability Act (HIPAA) governs the use and disclosure of Protected Health Information (PHI). Because well-generated synthetic data contains no actual patient identifiers and has no one-to-one link to a real person, it is generally not considered PHI.37 This classification is a major advantage, as it allows the data to be used and shared for research and development without the need for patient consent or the complex data use agreements and de-identification processes required for real data.3 This significantly accelerates development cycles and facilitates collaboration.
Guidance from Health Authorities
As synthetic data becomes more prevalent in medical research and regulatory submissions, health authorities like the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) are actively developing frameworks to govern its use.
The FDA recognizes the significant increase in submissions for drugs and medical devices that incorporate AI and synthetic data.75 The agency is actively studying the possibilities and limitations of supplementing real patient datasets with synthetic data, particularly for the development and assessment of AI models.77 In January 2025, the FDA released draft guidance on the use of AI in regulatory decision-making, which includes the expectation that sponsors provide a description of their use of synthetic data.78 While the FDA is open to the use of synthetic data for applications such as creating digital twins or synthetic control arms in clinical trials, it maintains that real-world data is still required to support final approval applications for new drugs and devices.80
The EMA, in conjunction with the Heads of Medicines Agencies (HMA), has established a workplan extending to 2028 that explicitly includes a review of “lesser-used data types” such as synthetic data and digital twins.81 The goal is to establish a shared understanding across the European regulatory network and to position their future use in medicines regulation. While formal guidelines are still in development, this indicates a clear intent to integrate these novel data sources into the regulatory process.57
Table 4 summarizes the current regulatory landscape.
| Regulatory Body/Framework | Stance on “Personal Data” | Key Guidance/Considerations | Status |
| GDPR / ICO (EU/UK) | Presumed to be personal data if derived from personal data, unless proven to be fully anonymous with remote re-identification risk.71 | The “reasonably likely to be used” test for re-identification is key. The ICO suggests differential privacy can help meet the anonymization threshold.60 | Evolving. The act of generation is regulated. The status of the output is context-dependent. |
| HIPAA (USA) | Generally not considered Protected Health Information (PHI) as it contains no real patient identifiers.37 | Enables freer use for research and development without patient consent or complex data use agreements. | Largely exempt, which accelerates innovation. |
| FDA (USA) | N/A (focus is on data credibility for regulatory decisions, not data protection status). | Draft guidance on AI use requires description of synthetic data. Actively studying its use for supplementing real data. Accepts synthetic control arms but requires real data for final approval.77 | Active development of a risk-based credibility assessment framework. |
| EMA (EU) | N/A (focus is on data utility for regulatory decisions). | Workplan to 2028 includes reviewing synthetic data to establish its future role in medicines regulation. No formal guidelines yet.81 | Exploratory. Acknowledged as an emerging data type for future integration. |
Beyond Compliance: A Framework for Ethical Governance
Adherence to regulations is necessary but not sufficient for the responsible use of synthetic data. A broader ethical framework is required to address concerns that fall outside the scope of data protection law. The four foundational principles of biomedical ethics provide a useful lens for this analysis 11:
- Respect for Autonomy: While direct patient consent may not be required for using synthetic data, this principle calls for transparency. Patients and the public should be informed about how their data contributes to the creation of generative models.
- Beneficence and Non-maleficence: These principles create a dual obligation: to actively contribute to patient welfare (beneficence) and to avoid causing harm (non-maleficence). In the context of synthetic data, this means ensuring that the data is of sufficient quality, accuracy, and representativeness to prevent the development of flawed or biased AI systems that could lead to misdiagnoses or inequitable care.11
- Justice: This principle requires the fair and equitable distribution of benefits and risks. The use of synthetic data must be scrutinized to ensure it does not lead to discrimination or worsen existing health disparities, for example, by creating AI models that work well for majority populations but fail for minorities.11
Even when individual privacy is technically preserved, significant ethical risks remain:
- Data Leakage and Re-identification: Despite the theoretical promise of anonymity, synthetic data is not immune to privacy risks. Generative models can sometimes “overfit” or “memorize” parts of their training data, especially for individuals with unique characteristics (outliers). This can lead to the generation of synthetic records that are too close to real ones, potentially leaking information that could be used for re-identification, particularly in partially synthetic datasets.83
- Group Harms: Perhaps the most subtle and significant ethical challenge is the risk of “group harm.” Even if no single individual can be identified, the aggregate statistical patterns replicated in synthetic data can reveal sensitive information about groups to which individuals belong. For example, an analysis of synthetic data might reveal a high prevalence of a certain condition within a specific demographic group. This information, even though derived from artificial data, could be used by entities like insurers or employers to discriminate against all individuals belonging to that group, causing harm based on statistical association rather than individual data.83
- Accountability and Trust: The use of a “black box” technology to generate data for training other “black box” AI models creates layers of opacity that challenge accountability. If an AI system trained on synthetic data makes a harmful error, who is responsible? The clinician who used the tool? The hospital that deployed it? The developer of the AI model? Or the creator of the synthetic data?.84 The careless or non-transparent use of synthetic data could erode the trust of both clinicians and the public in AI-driven medicine, hindering its adoption.54 This necessitates the development of standardized frameworks for measuring data quality and clear guidelines for appropriate use to ensure transparency and accountability.84
VII. The Future Horizon: Synthetic Data in Personalized and Public Health
The current applications of synthetic data, while transformative, largely focus on replicating static datasets to solve today’s problems of data access, privacy, and scale. However, the long-term trajectory of this technology points toward a more profound shift: from static replication to dynamic simulation. The future value of synthetic data lies not just in its ability to faithfully reproduce past data, but in its potential to create predictive, interactive models of biological systems—from a single virtual patient to an entire synthetic population. This evolution will transform synthetic data from a privacy-enhancing tool into a core scientific instrument for pioneering personalized medicine and reshaping public health strategy.
The Dawn of the ‘Virtual Patient’ and Digital Twins
The ultimate expression of synthetic data in personalized medicine is the concept of the “digital twin”—a dynamic, high-fidelity virtual model of an individual patient.33 This moves far beyond generating a single, static record. A digital twin is a longitudinal simulation, continuously updated with real-world data, that models an individual’s unique physiology, genetics, and lifestyle. It aims to replicate not just their current state, but their potential future trajectories under various conditions.85
These digital twins are poised to become a foundational technology for precision medicine, enabling a range of previously unattainable capabilities:
- Personalized Treatment Planning: Clinicians could use a patient’s digital twin to simulate their response to a variety of different drugs, dosages, or therapeutic strategies. This would allow them to identify the optimal, hyper-personalized treatment plan that maximizes efficacy and minimizes side effects before administering it to the real patient, moving away from a “one-size-fits-most” approach.33
- Predictive Modeling of Disease Progression: By running simulations on the digital twin, it would be possible to forecast the likely progression of a patient’s chronic disease, identify optimal windows for clinical intervention, and proactively manage their care.33
- In-Silico Experimentation: For a patient with a rare cancer, researchers could test novel, experimental compounds on their digital twin to gauge potential effectiveness. This allows for a form of virtual, personalized experimentation that reduces the risks and trial-and-error inherent in treating real subjects.33
From Digital Twins to Virtual Clinical Trials
The logical extension of the digital twin concept is the creation of entire “virtual clinical trials”.85 The vision is for future clinical research to be conducted partially or even wholly in silico, using cohorts of virtual patients. This could involve generating a synthetic treatment arm to explore a drug’s mechanism of action or, more radically, running a full trial where both the treatment and control groups are composed of synthetic digital twins.29
While fully virtual trials for regulatory approval remain a distant goal, the groundwork is already being laid. The successful use of synthetic control arms is a major step in this direction.29 The ability to simulate patient populations and predict outcomes is already shortening the path from a drug concept to a clinical trial, helping to de-risk development and optimize trial design.33 Realizing the full potential of virtual trials will require a significant paradigm shift from regulatory bodies, moving from a focus on evaluating outcomes in real patients to a new focus on rigorously validating the credibility and predictive power of the underlying simulation models themselves.85
Shaping Population Health: Long-Term Impact on Public Health
On a macro scale, synthetic data is set to become an indispensable tool for public health research and policy.
- Enhanced Surveillance and Predictive Modeling: The ability to generate large-scale, high-fidelity synthetic populations will provide public health officials with a powerful “sandbox” for modeling and planning. They will be able to run complex “what-if” scenarios with unprecedented speed and safety—simulating the spread of a novel pathogen under different containment strategies, forecasting healthcare demand during a pandemic, or evaluating the long-term impact of a nationwide vaccination or public health screening program.34
- Democratizing Access to Research Data: Large-scale longitudinal health studies, such as the UK Biobank, are invaluable resources for understanding the determinants of disease. However, access to this sensitive data is highly restricted. By creating and sharing high-quality synthetic versions of these biobanks, institutions can democratize access to this data. This would allow a much broader community of researchers, data scientists, and citizen scientists to work with the data, test hypotheses, and contribute to tackling major public health challenges without compromising participant privacy.86
- Proactively Addressing Health Inequities: As generative models become more sophisticated and fairness-aware, synthetic data will become a primary tool for studying and modeling health disparities. Researchers will be able to generate datasets that accurately reflect the diversity of the population, including underrepresented groups, and use these models to design and test interventions aimed at reducing health inequities and creating more just and effective public health policies.43
In essence, the long-term impact of synthetic data is not merely to solve the data access problem of today, but to create a new in-silico laboratory for medicine and public health. It will enable forms of experimentation, prediction, and personalization that are currently impossible, unethical, or simply too slow and expensive to conduct in the real world, fundamentally changing how we discover treatments, manage disease, and protect the health of populations.
VIII. Conclusion and Strategic Recommendations
Synthetic data has emerged as a technology of profound importance for the future of healthcare. It offers a paradigm-shifting approach to resolving the central conflict between the relentless demand for data to power AI and the sacrosanct need to protect patient privacy. By generating artificial datasets that preserve the statistical utility of real-world information without carrying individual identities, synthetic data acts as a powerful accelerator across the entire healthcare innovation lifecycle—from basic research and AI development to clinical trial design and public health modeling. Its ability to augment scarce datasets, particularly for rare diseases, and its potential to mitigate algorithmic bias by creating balanced training sets, underscore its transformative value.
However, this report has demonstrated that synthetic data is not a simple or risk-free solution. Its implementation is fraught with complexity, demanding a sophisticated understanding of the inherent trade-offs between data fidelity, utility, and privacy. The quality of synthetic data is not absolute but is “fit for purpose,” requiring bespoke validation frameworks for each specific use case. Furthermore, the risk of generative models amplifying inherited biases or introducing new ones is significant, posing a serious threat to health equity if not actively managed. The legal and ethical landscape remains nascent and complex, with the focus of accountability shifting from the protection of personal data to the downstream impact of the AI systems built upon it.
The journey toward responsible and effective adoption of synthetic data requires a concerted, multi-stakeholder effort. The following strategic recommendations are offered to guide this process.
Recommendations for Stakeholders
For Healthcare Organizations (CIOs, CTOs, and Governance Bodies)
- Establish a Tiered Governance Framework: Do not treat all synthetic data equally. Develop a clear, internal governance policy that categorizes the use of synthetic data based on risk.
- Low-Risk Tier (e.g., internal software testing, developer sandboxes): May require less stringent fidelity validation and can prioritize speed and scalability.
- Medium-Risk Tier (e.g., exploratory research, preliminary model training): Requires robust fidelity and utility validation (e.g., TSTR) and basic privacy checks (e.g., NNDR).
- High-Risk Tier (e.g., training clinical decision support models, generating synthetic control arms): Demands the most rigorous validation across all three dimensions of the trilemma, including adversarial privacy attacks and replication of clinical analyses. This tier should also include mandatory bias audits.
- Invest in Validation Expertise and Infrastructure: The generation of synthetic data is only half the challenge; validation is the other. Invest in building or acquiring the data science expertise and computational tools necessary to implement a comprehensive, use-case-specific validation protocol for every synthetic dataset produced or procured.
- Prioritize Transparency and Documentation: Mandate that all synthetic datasets are clearly labeled and accompanied by a “datasheet” that documents the source data, the generative model and parameters used, the results of all validation tests (fidelity, utility, and privacy), and a statement on its intended “fitness for purpose.” This is crucial for accountability and for preventing the inadvertent conflation of synthetic and real data.
For Researchers and Data Scientists
- Adopt a “Fitness-for-Purpose” Mindset: Reject the notion of a universally “good” synthetic dataset. Before using any synthetic data, rigorously define the specific requirements of the research question or model and validate the data against those specific needs. A dataset that is useful for one task may be misleading for another.
- Scrutinize for Bias: Do not assume synthetic data is inherently fair, even if designed to be. Actively probe for both inherited and introduced biases. Compare the performance of models trained on synthetic data across different demographic subgroups and employ fairness-aware generation techniques where possible.
- Champion Open Science in Synthesis: Advocate for and contribute to the development of open-source validation tools, standardized benchmark datasets for healthcare, and transparent reporting guidelines for research that uses synthetic data. Sharing methods and results will accelerate the development of best practices for the entire community.
For Regulators (FDA, EMA) and Policymakers
- Accelerate and Clarify Regulatory Guidance: Continue to develop clear, risk-based guidance on the use of synthetic data in regulatory submissions. This guidance should focus on establishing standards for model credibility, validation methodologies, and transparency in documentation, rather than prescribing specific generation techniques.
- Foster Public-Private Collaboration: Support and expand initiatives like the Synthia and SEARCH projects that bring together industry, academia, and regulatory bodies. These collaborations are essential for establishing consensus on quality standards, ethical best practices, and benchmark datasets for validation.
- Provide Legal Clarity: Work to reduce the legal ambiguity surrounding the status of different types of synthetic data under data protection laws like GDPR. Clearer definitions and safe harbors for high-quality, privacy-preserving synthetic data will encourage responsible innovation while ensuring fundamental rights are protected.
For Technology Developers (Synthetic Data Vendors)
- Build for Transparency and Auditability: Design synthetic data generation platforms that are not “black boxes.” Provide users with transparent controls over the generation process and detailed, auditable reports on the quality, privacy, and fairness characteristics of the output data.
- Integrate Fairness-as-a-Feature: Move beyond simply replicating source data. Integrate robust bias detection and mitigation tools directly into the generation workflow, allowing users to proactively create more equitable datasets.
- Communicate Trade-offs Clearly: Be transparent with users about the inherent trade-offs in synthetic data generation. Clearly document how different settings (e.g., enabling Differential Privacy) will impact the fidelity and utility of the output data, empowering users to make informed decisions based on their specific use case.