AI is penetrating almost every sector, and healthcare is no exception. But to create AI models specifically for healthcare use, the availability of health data is a major roadblock. Hospitals, pharmaceutical companies, and medical technology developers need extensive datasets to train algorithms, test applications, and investigate diseases. However, privacy restrictions, fragmented healthcare systems, data acquisition costs, and limited patient populations can constrain access.
Synthetic data has the potential to solve the data roadblocks hindering healthcare AI model development and adoption. But this does not mean the opportunity is just in generating data. Opportunities in the synthetic data in healthcare industry are expected to depend on whether synthetic datasets can deliver measurable utility, credible privacy protection, and reliable performance in healthcare applications. According to Kings Research, the global synthetic data in healthcare market is expected to rise at a 30.53% CAGR from 2026 to 2033 and reach a valuation of USD 1,062.8 million by 2033.
What Is Synthetic Data in Healthcare?
Artificially generated data or information related to healthcare using existing statistical patterns and relationships found in real patient data without directly reproducing the original records is known as synthetic data for healthcare. The data is designed to reproduce relevant characteristics of real patient data without representing a specific individual. Unlike conventional de-identification, which removes or modifies identifying information in existing records, synthetic data creates artificial records from learned patterns or statistical distributions.
How Is Synthetic Data Generated?
Statistical modeling methods, machine learning models, computer simulations, and privacy-enhancing techniques are used to generate synthetic health data. Artificial patient records, medical images, and clinical datasets that reproduce relevant characteristics of real-world data are the most in-demand data for healthcare companies. The selection of a synthetic data generation method depends on the available source data, required accuracy, and privacy constraints.
Statistical Modeling
This technique uses probability distributions, resampling, bootstrapping, and Bayesian networks to generate artificial records that retain selective characteristics of real health datasets. The technique is preferred for statistical analysis and research datasets, although complex clinical relationships may be difficult to preserve.
Machine Learning and Generative AI
Machine learning models and LLMs can learn patterns and relationships from existing medical images, clinical time-series data, patient records, or electronic health records (EHRs). By using this foundational data, generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models produce new synthetic records. Data generated from this method can be used for disease modeling, medical AI training, and data augmentation. This is particularly useful for limited and imbalanced real-world datasets.
Privacy-enhancing Techniques
Privacy protection or enhancement techniques can be embedded within the data generation process to reduce the risk of exposing sensitive patient information. For instance, differential privacy introduces mathematically controlled noise and privacy guarantees under defined conditions. Even if data is generated using other techniques, privacy assessment and evaluation are necessary before sharing and use.
Computer Simulations
These methods generate data from mathematical models of biological processes, clinical pathways, disease progression, or treatment responses. These methods are especially useful in research applications, where researchers can simulate different patient profiles and treatment scenarios to explore hypotheses, support drug development, and inform virtual clinical trials.
The data is now generated. This does not mean the data can be used directly in a healthcare application. Validation and verification of the data are very important before it can be used in any way. Evaluation of data fidelity, analytical usefulness, privacy risk, and representation across patient groups is necessary in determining whether the generated synthetic data is useful or not.
Interested in finding out more about synthetic data generation? Check out this Kings Research report on synthetic data generation to find information on where, other than healthcare, synthetic data is being used.
Which Healthcare Applications Are Creating Commercial Opportunities for Synthetic Data Providers?
Clinical research, diagnostics, precision medicine, rare disease research, and medical imaging are some of the most prominent commercial opportunities for synthetic health data companies. Individuals, businesses, and investors looking to determine which application to invest in should assess the scope of these commercial opportunities individually and in depth before investing in them. Here’s a brief overview of some of these key applications.
Medical Imaging and Diagnostic AI
Use of synthetic data and images for medical imaging and diagnostics algorithm development may emerge as a highly lucrative healthcare application segment. The need for large collections of annotated images covering different disease presentations and patient characteristics in this application makes synthetic data useful. Synthetic images can supplement training datasets, help test model robustness, and support evaluation across defined scenarios.
In May 2026, the U.S. Food and Drug Administration (FDA) released T-SYNTH, a dataset of 9,000 synthetic breast tomosynthesis images. The dataset is accessible to citizen scientists and researchers from various institutions, which is expected to help improve testing and development of AI tools used for breast cancer detection.
Rising use of AI in medical diagnostics is also creating new business scope for this application. However, synthetic images cannot completely replace real images derived from patient data. Evaluation, validation, and clinical reliability are still tied to real patient data and images.
The Kings Research report on the synthetic data in healthcare market states that the medical imaging and diagnostics segment was valued at USD 26.02 million in 2025.
Clinical Research and Drug Development
High clinical trial costs, limited patient populations, and restricted access to research data remain persistent challenges for the healthcare industry. Leveraging synthetic data to support exploratory analyses, trial simulations, protocol development, and the testing of analytical methods can create new opportunities.
This data cannot be used as a direct replacement for observed clinical outcomes or regulatory evidence. The suitability and acceptance of synthetic data depend on the research objective, validation approach, and applicable requirements.
EHR Analytics and Healthcare Software
Synthetic patient records and data can be used to test applications, build analytics pipelines, evaluate workflows, and troubleshoot system integrations. This reduces the exposure of real patient information, allowing developers a practical entry point before testing with real patient data. In these cases, synthetic data may be sufficient for software testing or workflow validation when clinical evidence is not required.
Precision Medicine and Rare-Disease Research
Underrepresented conditions and patient subgroups that are not widely researched can be covered by using synthetic datasets. The potential value of these datasets depends on their ability to preserve meaningful relationships between diagnoses, treatments, laboratory results, and outcomes. Rare disease clinical trials may become easier with the use of synthetic data.
Though these applications exhibit high commercial potential, investors and businesses looking to capitalize on them should evaluate certain parameters to ensure better ROI potential.
Which Factors Determine Demand for Synthetic Data in Healthcare?
Investment capacity, clinical research capabilities, and several other factors influence demand for synthetic data in the healthcare sector. Here are some key factors that all synthetic data generators and users in the healthcare sector should focus on before making a business decision.
- Data coverage: Support for structured records, imaging, clinical text, and other relevant data types.
- Privacy assessment: Evidence that disclosure and re-identification risks have been evaluated.
- Data utility: Performance on the intended research, testing, or AI development task.
- Bias and representativeness: Evidence that important patient groups and clinical patterns are preserved.
- Integration: Compatibility with existing healthcare data systems and workflows.
- Governance: Documentation, reproducibility, access controls, and ongoing monitoring.
What Do Investment Trends in Synthetic Health Data Point to?
The investment landscape for synthetic data in healthcare shows promise but also raises questions about commercial viability. Growing efforts to fast-track R&D across the healthcare sector have generated multiple opportunities for synthetic data adoption. Some of them are already being capitalized on by startups and public investments. Companies and investors that address persistent healthcare data-access gaps may be well positioned to benefit.
In October 2026, adsilico, a startup using AI and simulation to assess medical device performance in virtual patient populations, raised GBP 10 million in a Series A funding round. The company’s Virtual Chimera technology uses generative AI to build realistic virtual heart and blood vessel models using anonymized scans. Then, it runs physics-based simulations of how devices behave inside a model across a synthetic patient population.
In September 2026, BigHat Biosciences, a clinical-stage, AI-driven protein therapeutics platform provider, announced that it had completed USD 75 million in Series C financing. The new funding was expected to support the continued growth of the company’s leading AI-driven protein therapeutics platform and biological data generation. This is also expected to support BigHat’s pipeline of AI-designed therapeutics, including BHB810 and BHB299.
In March 2026, Mantis Biotech, an early-stage biotechnology company, raised USD 7.4 million in seed funding. The company is taking synthetic data one step further by using it to build “digital twins.” These digital twins are expected to be used in data aggregation and analysis to test new medical procedures, train medical robots, or predict/simulate healthcare conditions.
All of these investments show a promising outlook for synthetic data use in healthcare. However, this does not point to direct commercial and business success for companies involved. The majority of synthetic data applications are centered around innovation for now, and evaluating its commercial ROI conversion potential will be crucial as the market evolves.
What Should Investors and Businesses Interested in Synthetic Health Data Do?
Investors should monitor three signals: the transition from pilot projects to recurring commercial deployments, evidence that synthetic datasets improve downstream performance, and the development of credible privacy and validation frameworks.
Healthcare executives should prioritize use cases with clear business objectives, measurable outcomes, and manageable validation requirements. Vendors that combine data generation with evaluation, governance, and integration capabilities may be better positioned to address healthcare organizations' operational needs.
Want to understand more about how the synthetic data in healthcare industry could evolve in the future? Connect with analysts at Kings Research to obtain a detailed opportunity, risk, and ROI matrix for this industry.
Frequently Asked Questions
What factors influence the cost of implementing synthetic data in healthcare?
Implementation costs depend on data complexity, generation methods, computing infrastructure, integration with existing healthcare systems, and validation requirements. Organizations may also incur expenses for privacy assessments, regulatory compliance, and ongoing data quality monitoring. Total cost of ownership therefore depends on whether synthetic data is developed internally or purchased from a specialized provider.
Who are the major buyers in the synthetic data in healthcare market?
Major potential buyers include pharmaceutical companies, biotechnology firms, hospitals, academic medical centers, medical device manufacturers, healthcare AI developers, and contract research organizations. Their requirements differ: pharmaceutical companies may prioritize research and clinical development, while hospitals and AI developers may focus on analytics, software testing, and model development.
How can synthetic data generate revenue for healthcare technology companies?
Healthcare technology companies can commercialize synthetic data through subscription-based platforms, licensed datasets, data-generation services, customized modeling solutions, and enterprise software integrations. Revenue opportunities also exist in validation tools and privacy-risk assessment. Commercial success depends on demonstrable data utility, customer trust, integration capabilities, and compliance with applicable requirements.
What is the difference between synthetic healthcare data and anonymized healthcare data?
Anonymized healthcare data originates from real patient information that has undergone processing to reduce the possibility of identifying individuals. Synthetic healthcare data is artificially generated to reproduce selected patterns or relationships in source data, or to represent simulated scenarios. Both approaches require appropriate risk assessment; synthetic data is not automatically immune to disclosure or re-identification risks.
Which healthcare applications could create new growth opportunities for synthetic data providers?
Emerging opportunities include multimodal AI development, rare-disease research, digital twins, medical device testing, federated AI workflows, and the simulation of underrepresented patient populations. These applications may expand demand for specialized datasets, although adoption will depend on evidence of reliability, clinical relevance, and suitability for each use case.
What barriers could slow the growth of the synthetic data in healthcare market?
Market growth could be constrained by inconsistent evaluation standards, limited trust among healthcare researchers, integration costs, concerns about biased or unrealistic data, and uncertainty around regulatory acceptance. Organizations may also struggle to demonstrate that synthetic datasets improve model performance compared with approaches using real-world data alone.
What should healthcare organizations evaluate before selecting a synthetic data provider?
Buyers should assess data fidelity, downstream model performance, privacy-risk testing, demographic representation, documentation, interoperability, scalability, and total cost of ownership. They should also ask whether the provider validates datasets against independent real-world benchmarks and supplies evidence that the data is suitable for the intended research or operational purpose.
