Synthetic Data in Healthcare: Use Cases, Benefits, and Risks for AI and Analytics

With the market projected to reach a whopping USD 1,788.1 million by 2030 at a 35.3% annual growth rate, investing in synthetic data solutions means faster research, stronger compliance, and reduced costs.

Using synthetic data in healthcare is vital because it allows researchers and clinicians to work with large, realistic datasets without exposing sensitive patient details. As a result, synthetic data has become especially valuable in clinical trials, medical research, and the development of artificial intelligence (AI) tools in healthcare.

For example, synthetic patient records can be used to test new treatments or train diagnostic systems while keeping real patient data private. Or, predictive AI models can be trained for patient risk assessment using synthetic data, so privacy is ensured while still delivering actionable insights. 

What is Synthetic Data in Healthcare  

Synthetic data in healthcare refers to artificially generated information that closely mimics real patient data. Instead of being collected directly from hospitals or individuals, it is produced through advanced computer models that replicate the statistical patterns and clinical features found in medical records, diagnostic images, or physician notes.

Types of Synthetic Data in Healthcare

When discussing synthetic data in healthcare, it is important to understand the different levels at which it can be generated. Each type offers a unique balance between realism, privacy, and usability, depending on the needs of the organization.

• Fully Synthetic Data  

This type is created entirely by algorithms without using any real patient records. It is the safest option for privacy, as no actual patient information is involved. However, it may sometimes lack the fine details found in real-world data.

• Partially Synthetic Data  

Here, only certain sensitive fields are replaced with synthetic values, while the rest of the dataset remains real. This approach helps preserve accuracy and structure while protecting patient identities. It is often used when compliance is the main concern.

• Hybrid Synthetic Data  

Hybrid models combine real and synthetic elements. They allow healthcare organizations to retain the richness of real data while filling gaps with synthetic records. This balance makes hybrid data useful for AI training and clinical simulations.

Benefits of Synthetic Data in Medicine and Healthcare

And are there any tangible benefits that synthetic data offers to clinicians and researchers in their daily workflows? 

Yes, synthetic data:

• Ensures Compliance with Privacy Regulations  

Synthetic data allows hospitals and research teams to work with realistic datasets without exposing patient identities. This supports strict requirements under HIPAA and GDPR, reducing the risk of penalties and safeguarding patient trust.

• Enables Research on Rare Diseases  

Rare conditions often lack enough patient records for meaningful analysis. Here, synthetic data can generate sufficient sample sizes, giving researchers the ability to study treatments and outcomes that would otherwise be impossible to evaluate.

• Reduces Costs and optimizes AI Training  

Training AI models on real patient data is expensive and time‑consuming. Synthetic datasets lower costs by providing abundant, diverse records, helping businesses build and refine predictive tools faster without waiting for new data collection.

• Improves Testing Environments for Healthcare IT Systems  

Synthetic data offers safe, realistic test cases for healthcare software. It allows systems like EHR software or billing platforms to be tested thoroughly, ensuring accuracy and performance without exposing sensitive patient information.

• Enhances Security by Removing Identifiable Information  

As synthetic data does not contain actual patient identifiers, it reduces the chance of breaches or misuse. This makes it a secure option for AI analytics, training, and system integration across healthcare systems.

• Supports Scalability 

Use of synthetic data in healthcare systems means faster deployment of new services, reduced reliance on limited patient records, and the ability to expand AI‑driven solutions across larger networks. This directly supports efficiency, compliance, and patient care at scale. 

What are Some Use Cases Of Synthetic Data In Healthcare

Beyond these benefits, the true value of synthetic data becomes clear when we look at how it is applied in real healthcare settings. From medical imaging to clinical trial simulations, synthetic datasets are already reshaping workflows and reducing costs. The following use cases highlight where synthetic data is making the biggest impact.

1. Medical Imaging and Diagnostics

• Reducing Annotation Costs: Creating labeled medical images is expensive and time‑consuming. Synthetic radiology and pathology images help cut costs by providing ready‑to‑use datasets for AI training.

• Expanding Imaging Libraries: Advanced techniques generate diverse MRI and CT scans, giving diagnostic AI systems exposure to more scenarios and improving accuracy.

• Correcting Bias in Imaging: Synthetic datasets can balance representation across age, gender, and ethnicity, ensuring imaging tools perform reliably for all patient groups.

  
  

2. Clinical Trial Simulations  

Finding enough patients for clinical trials can take a lot of time and money. Synthetic data helps by creating “virtual” patient groups. Researchers can use these groups to test trial designs and predict results before working with real participants.

3. AI Training for Diagnostic Support  

Diagnostic AI systems need huge amounts of data to learn how to spot patterns in medical records in healthcare systems. Synthetic datasets provide this variety at scale, helping AI become more accurate while keeping patient identities safe.

4. Risk Prediction Models

Many hospitals rely on predictive analytics to identify patients at risk of complications. Synthetic data enables privacy‑preserving models, allowing forecasts without exposing sensitive health records.

5. Rare Disease Research and Drug Discovery

Rare conditions often lack enough patient data for meaningful study. Here, synthetic datasets generate larger samples, supporting research and drug discovery efforts that would otherwise be stalled due to limited scope.

6. Population Health Studies

Public health planning requires large‑scale datasets. Synthetic data can simulate epidemiological trends, helping policymakers evaluate current trends and allocate resources more effectively across diverse communities.

7. Pandemic Preparedness

Synthetic data on disease outbreaks allows health systems to model disease spread and test intervention strategies. This allows government organizations to stay prepared with realistic scenarios without relying on sensitive or incomplete real‑world data.

Synthetic Data Generation in Healthcare

Synthetic data generation is not a single step but a systematic workflow designed to ensure that the data produced is both scientifically valid and ethically safe. The process begins with preparing real clinical information, then applies advanced models to create artificial datasets that mirror the statistical properties of the original. Here’s how:

1. Data Collection and Preprocessing  

The first step begins with gathering real healthcare data, such as patient records, imaging scans, or laboratory results. This data is then cleaned and standardized to remove errors, inconsistencies, and sensitive identifiers. Preprocessing ensures that the source data is suitable for training synthetic data models.

2. Model Selection  

Different methods can be used to generate synthetic data. Common approaches include generative adversarial networks (GANs), variational autoencoders (VAEs), and statistical modeling techniques. 

The choice of model depends on the type of healthcare data being replicated. For example, GANs are often used for generating medical imaging data for medical software development, while statistical methods are better suited for structured/tabular clinical data.

3. Data Generation and Validation  

Once the model is trained, it produces synthetic datasets that mimic the patterns of the original data. Verification is critical at this stage: the synthetic data must be tested to confirm that it reflects real‑world distributions while maintaining patient privacy. It also checks that the data is accurate enough to support use in clinical research software.

4. Integration into AI and Analytics Pipelines  

Validated synthetic data is then integrated into healthcare workflows. It can be used to train diagnostic AI systems, support predictive analytics in healthcare, or test healthcare IT platforms. This integration ensures that synthetic data contributes directly to improving decision‑making and operational efficiency.

5. Continuous Monitoring for Bias and Accuracy  

Synthetic data is not static. Healthcare organizations must regularly monitor datasets to detect bias, inaccuracies, or drift over time. Continuous evaluation ensures that synthetic data remains representative of patient populations and continues to support fair and accurate trial results.

Understanding the Risks and Challenges of Synthetic Data for AI and Analytics

Synthetic data offers clear advantages, but its use in healthcare AI and analytics also introduces important risks. These challenges must be addressed to ensure that models trained on synthetic datasets remain trustworthy and clinically relevant.

• Quality Concerns 

The most pressing issue is the quality of synthetic datasets. AI and analytics depend on data that reflects the full complexity of healthcare scenarios, including rare cases and outliers. Synthetic data often smooths over these anomalies, which can weaken model robustness. As a result, predictions may miss critical signals, leading to inaccurate risk assessments or diagnostic errors.

• Bias Reproduction from Seed Data

Synthetic data is generated from existing datasets. If the original data contains demographic or clinical biases, these can be reproduced and even amplified. This creates the risk of unfair or skewed outcomes, such as unequal treatment recommendations across patient groups.

• Validation Difficulties

Unlike real-world data, synthetic datasets lack a clear ground truth. This makes verification a bit challenging, as there is no definitive way to confirm whether the generated data truly represents reality or not. Without strong validation, trust in synthetic data outputs remains limited.

• Privacy Risks

Although synthetic data is designed to protect patient identities, if it too closely resembles actual records, there is a risk of re-identification. This undermines its role as a privacy-preserving tool and can expose organizations to compliance violations.

• Over-Reliance on Synthetic Data

Synthetic datasets should complement, not replace, the real-world data. Relying too heavily on synthetic data may reduce the clinical relevance of findings and undermine confidence in AI systems, especially if those systems are not validated against real patient outcomes.

Regulatory Frameworks on the Usage of Synthetic Data in Healthcare

As synthetic data becomes more widely adopted in healthcare, regulators play a critical role in defining how it can be used responsibly. They recognize its potential to protect patient privacy while enabling research, but they also stress the importance of validation, transparency, and compliance.

• HIPAA 

Under HIPAA, synthetic data can be used in healthcare if it meets de‑identification standards. Regulators emphasize that synthetic datasets must be validated to ensure they cannot be traced back to individual patients. Documentation of workflows and privacy safeguards is required to demonstrate compliance.

• GDPR 

The GDPR views synthetic data as a privacy‑by‑design approach, provided it is proven to be non‑identifiable. Organizations must show transparency in how synthetic data is generated and used, especially in collaborative or cross‑border research. Auditable processes are essential to meet European regulatory expectations.

• FDA 

The FDA has begun exploring synthetic data in areas such as clinical trial simulations and medical device validation. While formal guidance is still evolving, the agency acknowledges its potential to accelerate innovation. Importantly, synthetic data should complement real‑world evidence, not replace it, in regulatory submissions.

• Governance in Hospitals and Research Institutions

Healthcare institutions are establishing governance frameworks to manage synthetic data responsibly. These frameworks include policies for generation, validation, and integration into AI workflows. Internal oversight ensures that synthetic data is applied ethically and supports clinical decision‑making without compromising patient trust.

Conclusion: Building Trust in Synthetic Data for Healthcare

Despite its growing adoption, synthetic data in healthcare still faces questions of validity and compliance. Policymakers continue to debate how closely synthetic datasets may resemble real records and what thresholds should apply to ensure regulatory approval. This uncertainty has led many healthcare organizations to hesitate, concerned about whether synthetic data can truly deliver reliable insights without compromising patient privacy.

Yet the successes already achieved from reducing imaging costs to accelerating trial simulations prove that synthetic data is not only viable but transformative. The challenge lies in ensuring that datasets are generated with foolproof methods, validated against clinical standards, and auditable for compliance.

Get complete, end‑to‑end support for AI model training, testing, and deployment in healthcare using verified synthetic datasets. Contact us today.

FAQs

1. Can synthetic data replace real data entirely?  

No. Synthetic data complements real data but cannot fully replace it. Real-world testing remains essential to ensure clinical relevance, accuracy, and trustworthiness in healthcare AI and analytics.

2. How accurate is synthetic data compared to real data?  

Synthetic data can closely mirror real datasets, but accuracy depends on model quality and validation. It may miss rare cases or subtle variations, so combining synthetic with real data ensures better outcomes.

3. What are the biggest challenges in generating synthetic data?  

The key challenges include maintaining realism, capturing rare events, preventing bias reproduction, and validating outputs without ground truth. It is similarly important to ensure privacy while avoiding overly close resemblance to real records.

4. How long does it take to generate synthetic data?  

The time taken to generate synthetic datasets depends on various factors like dataset size, complexity, and chosen model. Simple tabular data may be generated quickly, while medical imaging or large-scale patient records require longer training and validation cycles.

5. What methods are used for generating synthetic patient data?  

Common methods include generative adversarial networks (GANs), variational autoencoders (VAEs), and statistical modeling. Each technique is chosen based on the type of healthcare data, such as imaging, structured records, or population-level datasets.

6. Can synthetic data be used for regulatory compliance?  

Yes. Synthetic data supports compliance with HIPAA and GDPR by removing patient identifiers. However, health organizations must ensure that datasets are validated and that privacy safeguards are maintained to meet regulatory standards.