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July 2025
The global healthcare data synthesis tools market is on an upward trajectory, poised to generate substantial revenue growth, potentially climbing into the hundreds of millions over the forecast years from 2025 to 2034. This surge is attributed to evolving consumer preferences and technological advancements reshaping the industry.
The healthcare data synthesis tools market is primarily driven by the rising adoption of artificial intelligence (AI)/machine learning (ML) in the healthcare sector. The growing demand for high-quality and privacy-compliant data increases the use of healthcare data synthesis tools. Numerous government organizations support the adoption of digitization in healthcare through initiatives and funding. The future looks promising, with the integration of electronic health records (EHRs) and advancements in healthcare technology.
The market refers to the ecosystem of software platforms, algorithms, and frameworks used to integrate, harmonize, and simulate real-world healthcare data (clinical, operational, genomic, and claims data) for AI model training, population health managment, synthetic data generation, and privacy-preserving research. These tools enable interoperability across fragmented datasets, boost AI model performance, and reduce privacy risks by generating synthetic or federated datasets.
The explosion of healthcare data, the need for data privacy, and the increasing adoption of AI/ML in diagnostics, drug development, and care optimization drive market growth. Healthcare data synthesis tools enable healthcare professionals to drive innovation in various fields and revolutionize healthcare research. The growing need for data sharing in spite of the evolving regulatory landscape promotes the market. Favorable government support and increasing investments favor the adoption of digitalization in healthcare.
Generative artificial intelligence (GenAI) plays a vital role in healthcare data synthesis, helping researchers create realistic datasets while preserving patient privacy. It can analyze vast amounts of data, aiding in the generation of synthetic data for a large patient population. Generative adversarial networks (GANs) allow professionals to generate realistic imaging data, allowing them to train AI models without revealing real patient data. It saves researchers a lot of time and cost, thereby accelerating research in drug discovery and pandemic response. Moreover, GenAI can allow researchers to tailor datasets based on their specific needs.
Need for Privacy
The major growth factor of the healthcare data synthesis tools market is the growing need for privacy. Healthcare professionals need to maintain privacy in healthcare organizations due to the availability of highly sensitive patient data. Traditional de-identification tools fail to provide complete protection against privacy leaks. Healthcare data synthesis tools enable the generation of synthetic data that reproduces populations without real samples. This reduces the chances of data leakage and resolves privacy issues. Synthetic data can offer greater protection than real population datasets, enhancing patient trust and confidence in data sharing practices.
Challenges in Generating Patient Cohort
Healthcare data synthesis tools can generate a summary of a single patient based on a given set of characteristics. However, it is difficult for these tools to generate synthetic data or thousands of summaries for a large patient population. This inability to generate large synthetic data restricts market growth.
What is the Future of the Healthcare Data Synthesis Tools Market?
The market future is promising, driven by the increasing integration of EHRs in healthcare organizations. EHRs store patients’ health information in a digital format, comprising structured and unstructured data. Synthetic EHR data generation is an emerging solution to unlock the enormous research and educational potential of real-world healthcare data. It facilitates experimenting with the size of the real training and testing data over multiple replicates for accuracy and uncertainty assessments of methods. In the U.S., about 88% of hospitals have adopted EHR to streamline healthcare workflows.
By tool type, the data integration & harmonization platforms segment held a dominant presence in the market in 2024. This segment dominated because of the ability to enhance patient outcomes. Data harmonization enables more accurate and comprehensive diagnosis, personalized treatment plans, and improved patient care. The data integration and data harmonization platforms allow healthcare professionals to make informed clinical decisions. These platforms play a vital role in improving data accessibility and interoperability.
By tool type, the synthetic data generation tools segment is expected to grow at the fastest CAGR in the market during the forecast period. These tools generate synthetic data from real patient data. They aid in training ML models and other AI-based models, providing relevant data to healthcare professionals. They overcome several challenges, such as the availability of limited real patient data and the increasing demand for novel AI/ML-based products. They provide privacy of patient data and improve public health models to predict disease outbreaks.
By data source, the electronic health records (EHRs) segment held the largest revenue share of the market in 2024. This is due to the growing demand for EHRs in healthcare organizations and the need for improving workflows. EHRs can exchange health information electronically from one place to another. Synthetic data is generated to increase the accessibility of human data for different research purposes. AI/ML models trained using synthetic EHR data lead to enhanced model performance and reduced biases.
By data source, the patient-reported outcomes (PROs) segment is expected to grow with the highest CAGR in the market during the studied years. The increasing number of hospital admissions and clinical trials leads to excessive data generation. Synthetic tools can analyze PROs and generate clinically realistic synthetic patient health records. They are also beneficial in the case of rare disease patient data, wherein they can generate large amounts of synthetic data. PROs can be used to simulate care interventions and analyze longitudinal patient progress.
By application, the AI/ML model development & validation segment contributed the biggest revenue share of the market in 2024. This segment dominated due to the increasing use of AI/ML in healthcare and their potential benefits. The development and validation of AI/ML models require data for their functionalities. Synthetic data tools provide large amounts of data while maintaining the data privacy of real patients. AI/ML models can aid in patient diagnosis, suggesting personalized treatment, and monitor patients’ symptoms. They also predict disease outbreaks and assist in robotic surgery.
By application, the drug discovery & real-world evidence generation segment is expected to expand rapidly in the market in the coming years. Synthetic data generation tools create artificial datasets that mimic real-world conditions about patient conditions and behavior in a particular disease. This enables researchers to analyze data and develop novel drugs, providing personalized treatment. The rising prevalence of chronic and genetic disorders necessitates researchers to develop novel drugs.
By end-user, the healthcare providers & health systems segment led the global market in 2024. This is due to the increasing number of hospital admissions and the need to provide enhanced patient care. The presence of favorable infrastructure and suitable capital investment enables healthcare providers to adopt advanced technologies. The growing demand for personalized medicines and the need to maintain data privacy augment the segment’s growth.
By end-user, the AI/ML/HealthTech startups segment is expected to witness the fastest growth in the market over the forecast period. The increasing number of HealthTech startups and the rising development of AI/ML tools for healthcare purposes propel the segment’s growth. The growing venture capital investment supports startups, leading to the development of novel products. Synthetic data generation tools can bolster clinical research, application development, and data privacy protection efforts in the healthcare sector.
North America dominated the global market in 2024. The availability of a robust healthcare infrastructure, the presence of key players, and increasing investments are the major growth factors that govern market growth in North America. Government and private organizations invest in the development and deployment of AI/ML tools in healthcare organizations. The increasing adoption of EHRs and active regulatory pilots using synthetic data boosts the market.
Key players, such as Geisel Software, Inc., Veradigm, and MITRE Corporation, provide advanced synthetic data generation tools in the U.S. The Agency for Healthcare Research and Quality has a Synthetic Healthcare Database for Research (SyH-DR). It is a synthetic database that replicates the structure and statistical properties of the original claims data.
The Government of Canada announced an investment of $300 million for affordable access to computing power for small and medium-sized enterprises to develop made-in-Canada AI products and solutions as part of the AI Compute Access Fund. (Source - Canada) Montreal hosted the “Synthetic Data Summit 2025” in May 2025 to address real-world data challenges, advance privacy, and examine its future applications in healthcare. (Source - E Health Information)
Asia-Pacific is expected to host the fastest-growing healthcare data synthesis tools market in the coming years. The rapidly expanding healthcare sector and the rising adoption of advanced technologies drive digitization in healthcare organizations, favoring market growth. Countries like India, Japan, South Korea, and Australia are at the forefront of revolutionizing the healthcare sector in Asia-Pacific, providing improved patient care. The increasing number of healthcare startups and rising investments facilitate market growth. Healthcare providers focus on digitized health data due to the increasing population and rapidly changing demographics.
India has emerged as the third-largest startup ecosystem in the world. Out of the total 1.59 lakh startups, 2.04 lakh are related to IT services, and 1.47 lakh are related to healthcare & life sciences, as of January 2025. (Source - Azadi ka Amrut Mahotsav)The Healthtech sector in India saw a strong recovery in 2024, with total capital raised increasing to $1.13 billion across 112 deals.
There are a total of 1,251 HealthTech startups in South Korea, of which 271 companies collectively raised $1.06 billion in venture capital money and private equity. The federal government recently announced a five-year roadmap (2025-2028) to propel R&D in AI for healthcare. The country aims to leverage cutting-edge technology to enhance public health and well-being. (Source - Global Pricing)
Europe is considered to be a significantly growing area in the healthcare data synthesis tools market. The presence of advanced healthcare infrastructure and favorable government support augments market growth. Government organizations have launched initiatives to introduce digitization in the healthcare sector. The increasing investments and collaborations among key players lead to the development of novel AI models and access to cutting-edge technologies. The European Union supports a project, “SYNTHIA,” to deliver validated, reliable tools and methods for synthetic data generation (SDG) with a total funding of €12.43 million. (Source - Innovative Health Initiative)
The German data protection authorities (DPAs) issued a number of guidance documents related to the development and operation of AI systems. The German government launched the Act to Accelerate the Digitalization of the Healthcare System (Digital Act) and the Act on the improved Use of Health Data. The latter act focuses on progressing and improving the use of data for research and innovation in healthcare. (Source - Federal Ministry of Health)
In June 2025, the UK became the first country in the world to join a new global network of health regulators focused on the safe, effective use of AI in healthcare. The Medicines and Healthcare products Regulatory Agency (MHRA) will help shape international rules for AI in healthcare, supporting earlier diagnosis, cutting NHS waiting times, and backing growth in the UK’s healthtech sector. (Source - Gov.uk)
Rhys Parker, Chief Clinical Information Officer at SA Health, commented that the company has embraced synthetic data as a forward-thinking, privacy-conscious approach to safe EMR data sharing for clinical decision-making and training ML models. The integration of Gretel in the company’s Azure environment improves care for patients with a focus on inclusivity and privacy protection. (Source - Microsoft)
By Tool Type
By Data Source
By Application
By End-User
By Region
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