BackgroundClinical data is instrumental to medical research, machine learning (ML) model development, and advancing surgical care, but access is often constrained by privacy regulations and missing data. Synthetic data offers a promising solution to preserve privacy while enabling broader data access. Recent advances in large language models (LLMs) provide an opportunity to generate synthetic data with reduced reliance on domain expertise, computational resources, and pre-training.ObjectiveThis study aims to assess the feasibility of generating realistic tabular clinical data with OpenAI’s GPT-4o using zero-shot prompting, and evaluate the fidelity of LLM-generated data by comparing its statistical properties to the Vital Signs DataBase (VitalDB), a real-world open-source perioperative dataset.MethodsIn Phase 1, GPT-4o was prompted to generate a dataset with qualitative descriptions of 13 clinical parameters. The resultant data was assessed for general errors, plausibility of outputs, and cross-verification of related parameters. In Phase 2, GPT-4o was prompted to generate a dataset using descriptive statistics of the VitalDB dataset. Fidelity was assessed using two-sample t-tests, two-sample proportion tests, and 95% confidence interval (CI) overlap.ResultsIn Phase 1, GPT-4o generated a complete and structured dataset comprising 6,166 case files. The dataset was plausible in range and correctly calculated body mass index for all case files based on respective heights and weights. Statistical comparison between the LLM-generated datasets and VitalDB revealed that Phase 2 data achieved significant fidelity. Phase 2 data demonstrated statistical similarity in 12/13 (92.31%) parameters, whereby no statistically significant differences were observed in 6/6 (100.0%) categorical/binary and 6/7 (85.71%) continuous parameters. Overlap of 95% CIs were observed in 6/7 (85.71%) continuous parameters.ConclusionZero-shot prompting with GPT-4o can generate realistic tabular synthetic datasets, which can replicate key statistical properties of real-world perioperative data. This study highlights the potential of LLMs as a novel and accessible modality for synthetic data generation, which may address critical barriers in clinical data access and eliminate the need for technical expertise, extensive computational resources, and pre-training. Further research is warranted to enhance fidelity and investigate the use of LLMs to amplify and augment datasets, preserve multivariate relationships, and train robust ML models.

Dataset used in the article entitled 'Synthetic Datasets Generator for Testing Information Visualization and Machine Learning Techniques and Tools'. These datasets can be used to test several characteristics in machine learning and data processing algorithms.

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Synthetic Data Generation Market Size 2024-2028

The synthetic data generation market size is forecast to increase by USD 2.88 billion at a CAGR of 60.02% between 2023 and 2028.

The global synthetic data generation market is expanding steadily, driven by the growing need for privacy-compliant data solutions and advancements in AI technology. Key factors include the increasing demand for data to train machine learning models, particularly in industries like healthcare services and finance where privacy regulations are strict and the use of predictive analytics is critical, and the use of generative AI and machine learning algorithms, which create high-quality synthetic datasets that mimic real-world data without compromising security.
This report provides a detailed analysis of the global synthetic data generation market, covering market size, growth forecasts, and key segments such as agent-based modeling and data synthesis. It offers practical insights for business strategy, technology adoption, and compliance planning. A significant trend highlighted is the rise of synthetic data in AI training, enabling faster and more ethical development of models. One major challenge addressed is the difficulty in ensuring data quality, as poorly generated synthetic data can lead to inaccurate outcomes.
For businesses aiming to stay competitive in a data-driven global landscape, this report delivers essential data and strategies to leverage synthetic data trends and address quality challenges, ensuring they remain leaders in innovation while meeting regulatory demands

What will be the Size of the Market During the Forecast Period?

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Synthetic data generation offers a more time-efficient solution compared to traditional methods of data collection and labeling, making it an attractive option for businesses looking to accelerate their AI and machine learning projects. The market represents a promising opportunity for organizations seeking to overcome the challenges of data scarcity and privacy concerns while maintaining data diversity and improving the efficiency of their artificial intelligence and machine learning initiatives. By leveraging this technology, technology decision-makers can drive innovation and gain a competitive edge in their respective industries.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Healthcare and life sciences
  Retail and e-commerce
  Transportation and logistics
  IT and telecommunication
  BFSI and others


Type

  Agent-based modelling
  Direct modelling


Data

  Tabular Data
  Text Data
  Image & Video Data
  Others


Offering Band

  Fully Synthetic Data
  Partially Synthetic Data
  Hybrid Synthetic Data


Application

  Data Protection
  Data Sharing
  Predictive Analytics
  Natural Language Processing
  Computer Vision Algorithms
  Others


Geography

  North America

    US
    Canada
    Mexico


  Europe

    Germany
    UK
    France
    Italy


  APAC

    China
    Japan
    India


  Middle East and Africa



  South America

By End-user Insights

The healthcare and life sciences segment is estimated to witness significant growth during the forecast period. In the thriving healthcare and life sciences sector, synthetic data generation is gaining significant traction as a cost-effective and time-efficient alternative to utilizing real-world data. This market segment's rapid expansion is driven by the increasing demand for data-driven insights and the importance of safeguarding sensitive information. One noteworthy application of synthetic data generation is in the realm of computer vision, specifically with geospatial imagery and medical imaging.

For instance, in healthcare, synthetic data can be generated to replicate medical imaging, such as MRI scans and X-rays, for research and machine learning model development without compromising patient privacy. Similarly, in the field of physical security, synthetic data can be employed to enhance autonomous vehicle simulation, ensuring optimal performance and safety without the need for real-world data. By generating artificial datasets, organizations can diversify their data sources and improve the overall quality and accuracy of their machine learning models.

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The healthcare and life sciences segment was valued at USD 12.60 million in 2018 and showed a gradual increase during the forecast period.

Regional Insights

North America is estimated to contribute 36% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the m

Objective: Biomechanical Machine Learning (ML) models, particularly deep-learning models, demonstrate the best performance when trained using extensive datasets. However, biomechanical data are frequently limited due to diverse challenges. Effective methods for augmenting data in developing ML models, specifically in the human posture domain, are scarce. Therefore, this study explored the feasibility of leveraging generative artificial intelligence (AI) to produce realistic synthetic posture data by utilizing three-dimensional posture data.Methods: Data were collected from 338 subjects through surface topography. A Variational Autoencoder (VAE) architecture was employed to generate and evaluate synthetic posture data, examining its distinguishability from real data by domain experts, ML classifiers, and Statistical Parametric Mapping (SPM). The benefits of incorporating augmented posture data into the learning process were exemplified by a deep autoencoder (AE) for automated feature representation.Results: Our findings highlight the challenge of differentiating synthetic data from real data for both experts and ML classifiers, underscoring the quality of synthetic data. This observation was also confirmed by SPM. By integrating synthetic data into AE training, the reconstruction error can be reduced compared to using only real data samples. Moreover, this study demonstrates the potential for reduced latent dimensions, while maintaining a reconstruction accuracy comparable to AEs trained exclusively on real data samples.Conclusion: This study emphasizes the prospects of harnessing generative AI to enhance ML tasks in the biomechanics domain.

The global synthetic data market size is projected to grow from USD 0.4 billion in the current year to USD 19.22 billion by 2035, representing a CAGR of 42.14%, during the forecast period till 2035

Ainnotate’s proprietary dataset generation methodology based on large scale generative modelling and Domain randomization provides data that is well balanced with consistent sampling, accommodating rare events, so that it can enable superior simulation and training of your models.

Ainnotate currently provides synthetic datasets in the following domains and use cases.

Internal Services - Visa application, Passport validation, License validation, Birth certificates Financial Services - Bank checks, Bank statements, Pay slips, Invoices, Tax forms, Insurance claims and Mortgage/Loan forms Healthcare - Medical Id cards

Synthetic Data Generation Market Size

As per the latest insights from Market.us, the Global Synthetic Data Generation Market is set to reach USD 6,637.98 million by 2034, expanding at a CAGR of 35.7% from 2025 to 2034. The market, valued at USD 313.50 million in 2024, is witnessing rapid growth due to rising demand for high-quality, privacy-compliant, and AI-driven data solutions.

North America dominated in 2024, securing over 35% of the market, with revenues surpassing USD 109.7 million. The region’s leadership is fueled by strong investments in artificial intelligence, machine learning, and data security across industries such as healthcare, finance, and autonomous systems. With increasing reliance on synthetic data to enhance AI model training and reduce data privacy risks, the market is poised for significant expansion in the coming years.

Market Analysis for Synthetic Data Solution The global synthetic data solution market is projected to reach USD XXX million by 2033, growing at a CAGR of XX% from 2025 to 2033. The increasing demand for synthetic data in various industries, such as financial services, retail, and healthcare, drives this growth. Synthetic data offers a privacy-preserving alternative to real-world data, enabling organizations to train and evaluate models without compromising sensitive information. The growing adoption of cloud-based solutions and the increasing need for data privacy and security further contribute to market growth. Market segments include deployment types (cloud-based and on-premises) and applications (financial services industry, retail industry, medical industry, and others). Key regional markets include North America, South America, Europe, Middle East & Africa, and Asia Pacific. Major companies operating in the market include LightWheel AI, Hanyi Innovation Technology, Haohan Data Technology, Haitian Ruisheng Science Technology, and Baidu. Trends such as the adoption of artificial intelligence (AI) and machine learning (ML) and the rising concern over data privacy and governance are expected to shape the market's future.

The Synthetic Data Generation Marketsize was valued at USD 288.5 USD Million in 2023 and is projected to reach USD 1920.28 USD Million by 2032, exhibiting a CAGR of 31.1 % during the forecast period.Synthetic data generation stands for the generation of fake datasets that resemble real datasets with reference to their data distribution and patterns. It refers to the process of creating synthetic data points utilizing algorithms or models instead of conducting observations or surveys. There is one of its core advantages: it can maintain the statistical characteristics of the original data and remove the privacy risk of using real data. Further, with synthetic data, there is no limitation to how much data can be created, and hence, it can be used for extensive testing and training of machine learning models, unlike the case with conventional data, which may be highly regulated or limited in availability. It also helps in the generation of datasets that are comprehensive and include many examples of specific situations or contexts that may occur in practice for improving the AI system’s performance. The use of SDG significantly shortens the process of the development cycle, requiring less time and effort for data collection as well as annotation. It basically allows researchers and developers to be highly efficient in their discovery and development in specific domains like healthcare, finance, etc. Key drivers for this market are: Growing Demand for Data Privacy and Security to Fuel Market Growth. Potential restraints include: Lack of Data Accuracy and Realism Hinders Market Growth. Notable trends are: Growing Implementation of Touch-based and Voice-based Infotainment Systems to Increase Adoption of Intelligent Cars.

This image dataset contains synthetic structure images used for training the deep-learning based nanowire segmentation model presented in our work "A deep learned nanowire segmentation model using synthetic data augmentation" to be published in npj Computational materials. Detailed information can be found in the corresponding article.

According to a survey of artificial intelligence (AI) companies in South Korea carried out in 2023, nearly 66 percent of the data used when developing AI products and services was private data. On the other hand, public data comprised around 34 percent.

The global synthetic data tool market is projected to reach USD 10,394.0 million by 2033, exhibiting a CAGR of 34.8% during the forecast period. The growing adoption of AI and ML technologies, increasing demand for data privacy and security, and the rising need for data for training and testing machine learning models are the key factors driving market growth. Additionally, the availability of open-source synthetic data generation tools and the increasing adoption of cloud-based synthetic data platforms are further contributing to market growth. North America is expected to hold the largest market share during the forecast period due to the early adoption of AI and ML technologies and the presence of key vendors in the region. Europe is anticipated to witness significant growth due to increasing government initiatives to promote AI adoption and the growing data privacy concerns. The Asia Pacific region is projected to experience rapid growth due to government initiatives to develop AI capabilities and the increasing adoption of AI and ML technologies in various industries, namely healthcare, retail, and manufacturing.

This model learning dataset is created out of the Raw Synthetic RWD raw dataset, including some of the original attributes. It is distributed in JOBLIB files, where .joblib files contain the vectors and _ids.joblib contain the ID of the person from which each vector is extracted.

This is useful in case it is needed to map the vectors to metadata about the people that are found in the original raw dataset. Note that corresponds to , or , depending on the dataset. The split is roughly 60% of the people are in the training dataset, and 20% in each of the validation and the testing datasets. The input attributes are the age, the short-term averages and the trends of the current week’s BMI, steps walked, calories burned, sleep quality, mood and water consumption, as well as the previous week’s short-term average and trend of the answer to the health self-assessment question.

The outcome to be predicted is the binary quantized health self-assessment answer to be given in the current week. The dataset is normalized based on the training set. The means and standard deviations used can be found in the train_statistics.joblib file. Finally, the output_descriptions.joblib file contains descriptions of the outcomes to be predicted (not actually needed, since included here).

The existing datasets lack the diversity required to train the model so that it performs equally well in real fields under varying environmental conditions. To address this limitation, we propose to collect a small number of in-field data and use the GAN to generate synthetic data for training the deep learning network. To demonstrate the proposed method, a maize dataset 'IIITDMJ_Maize' was collected using a drone camera under different weather conditions, including both sunny and cloudy days. The recorded video was processed to sample image frames that were later resized to 224 x 224. Keeping some raw images intact for evaluation purpose, images were further processed to crop only the portion containing diseases and selecting healthy plant images. With the help of agriculture experts, the raw and cropped images were subsequently categorized into four distinct classes -- (a) common rust, (b) northern leaf blight, (c) gray leaf spot, and (d) healthy. In total, 416 images were collected and labeled. Further, 50 raw (un-cropped) images of each category were also selected for testing the model's performance.

The DJIN model of aging was trained on the English Longitudinal Study of Aging (ELSA). Here we have used the model to generate a large synthetic population of 9 million individuals. There are 3 million individuals for each baseline age of 65, 75, and 85 years simulated for 20 years. For each individual, we supply a health trajectory with 29 tracked health variables with mortality. Demographic and background health variables have been sampled based on the ELSA population demographics.

Each Data_part includes 1.8 million individuals. The file_description.txt file describes the files, and health_columns.csv and background_columns.csv indicate the columns of the files.

The ELSA dataset itself can be accessed at https://www.elsa-project.ac.uk/accessing-elsa-data.

Code for the model is available at https://github.com/Spencerfar/djin-aging.

This dataset contains Synthea synthetic patient data used in building ML models for lung cancer risk prediction. The ML models are used to simulate ML-enabled LHS. This open dataset is part of the synthetic data repository of the Open LHS project on GitHub: https://github.com/lhs-open/synthetic-data. For data source and methods, see the first ML-LHS simulation paper published in Nature Scientific Reports: https://www.nature.com/articles/s41598-022-23011-4.

Data scarcity presents a significant obstacle in the field of biomedicine, where acquiring diverse and sufficient datasets can be costly and challenging. Synthetic data generation offers a potential solution to this problem by expanding dataset sizes, thereby enabling the training of more robust and generalizable machine learning models. Although previous studies have explored synthetic data generation for cancer diagnosis, they have predominantly focused on single-modality settings, such as whole-slide image tiles or RNA-Seq data. To bridge this gap, we propose a novel approach, RNA-Cascaded-Diffusion-Model or RNA-CDM, for performing RNA-to-image synthesis in a multi-cancer context, drawing inspiration from successful text-to-image synthesis models used in natural images. In our approach, we employ a variational auto-encoder to reduce the dimensionality of a patient’s gene expression profile, effectively distinguishing between different types of cancer. Subsequently, we employ a cascad..., , , # RNA-CDM Generated One Million Synthetic Images

https://doi.org/10.5061/dryad.6djh9w174

One million synthetic digital pathology images were generated using the RNA-CDM model presented in the paper "RNA-to-image multi-cancer synthesis using cascaded diffusion models".

Description of the data and file structure

There are ten different h5 files per cancer type (TCGA-CESC, TCGA-COAD, TCGA-KIRP, TCGA-GBM, TCGA-LUAD). Each h5 file contains 20.000 images. The key is the tile number, ranging from 0-20,000 in the first file, and from 180,000-200,000 in the last file. The tiles are saved as numpy arrays.

Code/Software

The code used to generate this data is available under academic license in https://rna-cdm.stanford.edu .

Manuscript citation

Carrillo-Perez, F., Pizurica, M., Zheng, Y. et al. Generation of synthetic whole-slide image tiles of tumours from RNA-sequencing data via cascaded diffusion models...

The global Artificial Intelligence (AI) Training Dataset market is projected to reach $1605.2 million by 2033, exhibiting a CAGR of 9.4% from 2025 to 2033. The surge in demand for AI training datasets is driven by the increasing adoption of AI and machine learning technologies in various industries such as healthcare, financial services, and manufacturing. Moreover, the growing need for reliable and high-quality data for training AI models is further fueling the market growth. Key market trends include the increasing adoption of cloud-based AI training datasets, the emergence of synthetic data generation, and the growing focus on data privacy and security. The market is segmented by type (image classification dataset, voice recognition dataset, natural language processing dataset, object detection dataset, and others) and application (smart campus, smart medical, autopilot, smart home, and others). North America is the largest regional market, followed by Europe and Asia Pacific. Key companies operating in the market include Appen, Speechocean, TELUS International, Summa Linguae Technologies, and Scale AI. Artificial Intelligence (AI) training datasets are critical for developing and deploying AI models. These datasets provide the data that AI models need to learn, and the quality of the data directly impacts the performance of the model. The AI training dataset market landscape is complex, with many different providers offering datasets for a variety of applications. The market is also rapidly evolving, as new technologies and techniques are developed for collecting, labeling, and managing AI training data.

The U.S. AI Training Dataset Market size was valued at USD 590.4 million in 2023 and is projected to reach USD 1880.70 million by 2032, exhibiting a CAGR of 18.0 % during the forecasts period. The U. S. AI training dataset market deals with the generation, selection, and organization of datasets used in training artificial intelligence. These datasets contain the requisite information that the machine learning algorithms need to infer and learn from. Conducts include the advancement and improvement of AI solutions in different fields of business like transport, medical analysis, computing language, and money related measurements. The applications include training the models for activities such as image classification, predictive modeling, and natural language interface. Other emerging trends are the change in direction of more and better-quality, various and annotated data for the improvement of model efficiency, synthetic data generation for data shortage, and data confidentiality and ethical issues in dataset management. Furthermore, due to arising technologies in artificial intelligence and machine learning, there is a noticeable development in building and using the datasets. Recent developments include: In February 2024, Google struck a deal worth USD 60 million per year with Reddit that will give the former real-time access to the latter’s data and use Google AI to enhance Reddit’s search capabilities. , In February 2024, Microsoft announced around USD 2.1 billion investment in Mistral AI to expedite the growth and deployment of large language models. The U.S. giant is expected to underpin Mistral AI with Azure AI supercomputing infrastructure to provide top-notch scale and performance for AI training and inference workloads. .

TiCaM Synthectic Images: A Time-of-Flight In-Car Cabin Monitoring Dataset is a time-of-flight dataset of car in-cabin images providing means to test extensive car cabin monitoring systems based on deep learning methods. The authors provide a synthetic image dataset of car cabin images similar to the real dataset leveraging advanced simulation software’s capability to generate abundant data with little effort. This can be used to test domain adaptation between synthetic and real data for select classes. For both datasets the authors provide ground truth annotations for 2D and 3D object detection, as well as for instance segmentation.