# Synthetic Data * **Definition:** Artificially generated data that mimics real patient data while preserving privacy and security, often used for research, analysis, and training AI models effectively. * **Taxonomy:** CTO Topics / Synthetic Data ## News * Selected news on the topic of **Synthetic Data**, for healthcare technology leaders * 2.5K news items are in the system for this topic * Posts have been filtered for tech and healthcare-related keywords | Date | Title | Source | | --- | --- | --- | | 4/30/2025 | [**How an Israeli Health System Launched the Nation's First Virtual Hospital Program**](https://www.healthitanswers.net/how-an-israeli-health-system-launched-the-nations-first-virtual-hospital-program/) | [[Health IT Answers]] | | 4/14/2025 | [**Synthetic Data's Impact On AI**](https://www.forbes.com/councils/forbestechcouncil/2025/04/14/synthetic-datas-impact-on-ai/) | [[Forbes]] | | 4/8/2025 | [**Tonic.ai Signs Strategic Collaboration Agreement with AWS to Advance Responsible AI Innovation**](https://finance.yahoo.com/news/tonic-ai-signs-strategic-collaboration-140400376.html) | [[Yahoo Finance]] | | 3/20/2025 | [**Global GenAI study reveals optimism and opportunities for health care and life sciences**](https://www.prnewswire.com/news-releases/global-genai-study-reveals-optimism-and-opportunities-for-health-care-and-life-sciences-302407154.html) | [[PR Newswire]] | | 3/20/2025 | [**Global GenAI study reveals optimism and opportunities for health care and life sciences**](https://www.morningstar.com/news/pr-newswire/20250320cl45976/global-genai-study-reveals-optimism-and-opportunities-for-health-care-and-life-sciences) | [[Morningstar]] | | 3/10/2025 | [**Will synthetic data derail generative AI's momentum or be the breakthrough we need?**](https://www.zdnet.com/article/will-synthetic-data-derail-generative-ais-momentum-or-be-the-breakthrough-we-need/) | [[ZDNet]] | | 3/6/2025 | [**SmartOne.ai Unveils Cutting-Edge Synthetic Data Solutions for the AI Era - Morningstar**](https://www.morningstar.com/news/pr-newswire/20250306to35602/smartoneai-unveils-cutting-edge-synthetic-data-solutions-for-the-ai-era) | [[Morningstar]] | | 3/6/2025 | [**SmartOne.ai Unveils Cutting-Edge Synthetic Data Solutions for the AI Era - PR Newswire**](https://www.prnewswire.com/news-releases/smartoneai-unveils-cutting-edge-synthetic-data-solutions-for-the-ai-era-302395063.html) | [[PR Newswire]] | | 3/6/2025 | [**SmartOne.ai Unveils Cutting-Edge Synthetic Data Solutions for the AI Era**](https://finance.yahoo.com/news/smartone-ai-unveils-cutting-edge-195800100.html) | [[Yahoo Finance]] | | 2/20/2025 | [**Governing synthetic data in medical research: the time is now - The Lancet Digital Health**](https://www.thelancet.com/journals/landig/article/PIIS2589-7500(25)00011-1/fulltext?rss=yes) | [[The Lancet]] | | 2/19/2025 | [**Synthetic data takes aim at AI training challenges - CIO**](https://www.cio.com/article/3827383/synthetic-data-takes-aim-at-ai-training-challenges.html) | [[CIO]] | | 2/14/2025 | [**Engineering a Healthcare Analytics Center of Excellence (ACoE): A Strategic Framework for ...**](https://blogs.perficient.com/2025/02/14/engineering-a-healthcare-analytics-center-of-excellence-acoe-a-strategic-framework-for-innovation/) | [[Perficient Healthcare]] | | 1/26/2025 | [**A scoping review of privacy and utility metrics in medical synthetic data - npj Digital Medicine**](https://www.nature.com/articles/s41746-024-01359-3) | [[Nature]] | | 1/13/2025 | [**Synthetic Data Generation Research Report, 2023 & 2024-2030: Growing Development ...**](https://finance.yahoo.com/news/synthetic-data-generation-research-report-113100722.html) | [[Yahoo Finance]] | | 1/13/2025 | [**Synthetic Data Generation Business Research Report 2024: Global Market to Reach $3.7 Billion by 2030 from $323 Million in 2023, Driven by Rising Demand for Data Privacy and Anonymization Solutions...**](http://www.businesswire.com/news/home/20250113130135/en/Synthetic-Data-Generation-Business-Research-Report-2024-Global-Market-to-Reach-3.7-Billion-by-2030-from-323-Million-in-2023-Driven-by-Rising-Demand-for-Data-Privacy-and-Anonymization-Solutions---ResearchAndMarkets.com/?feedref=JjAwJuNHiystnCoBq_hl-Q-tiwWZwkcswR1UZtV7eGe24xL9TZOyQUMS3J72mJlQ7fxFuNFTHSunhvli30RlBNXya2izy9YOgHlBiZQk2LOzmn6JePCpHPCiYGaEx4DL1Rq8pNwkf3AarimpDzQGuQ==) | [[Business Wire]] | | 1/13/2025 | [**Synthetic Data Generation Business Research Report 2024: Global Market to Reach $3.7 Billion by 2030 from $323 Million in 2023, Driven by Rising Demand for Data Privacy and ...**](https://www.businesswire.com/news/home/20250113130135/en/) | [[Business Wire]] | | 11/27/2024 | [**Artificial Intelligence - Healthcare IT News**](https://www.healthcareitnews.com/taxonomy/term/7341/m89gsv6dzcjz.jsp/page/222?type=video) | [[Healthcare IT News]] | | 11/26/2024 | [**The urgent need to accelerate synthetic data privacy frameworks for medical research**](https://www.thelancet.com/journals/landig/article/PIIS2589-7500(24)00196-1/fulltext?rss=yes) | [[The Lancet]] | | 11/26/2024 | [**The urgent need to accelerate synthetic data privacy frameworks for medical research**](https://www.thelancet.com/journals/landig/article/PIIS2589-7500(24)00196-1/fulltext) | [[The Lancet]] | | 11/16/2024 | [**Clearly smart, SAS acquires Hazy: A wider vision for synthetic data - Computer Weekly**](https://www.computerweekly.com/blog/CW-Developer-Network/Clearly-smart-SAS-acquires-Hazy-A-wider-vision-for-synthetic-data) | [[Computer Weekly]] | | 11/12/2024 | [**SAS acquires Hazy synthetic data software to boost generative AI portfolio - PR Newswire**](https://www.prnewswire.com/news-releases/sas-acquires-hazy-synthetic-data-software-to-boost-generative-ai-portfolio-302300944.html) | [[PR Newswire]] | | 8/26/2024 | [**Synthetic Data Generation Market Surpasses USD 3.79 By 2032 Driven Due To Escalating ...**](https://finance.yahoo.com/news/synthetic-data-generation-market-surpasses-130000815.html) | [[Yahoo Finance]] | | 8/12/2024 | [**4 high-value use cases for synthetic data in healthcare - TechTarget**](https://www.techtarget.com/healthtechanalytics/feature/High-value-use-cases-for-synthetic-data-in-healthcare) | techtarget.com | | 7/12/2024 | [**What Kind Of Synthetic Data Should My Company Use?**](https://www.forbes.com/sites/forbestechcouncil/2024/07/12/what-kind-of-synthetic-data-should-my-company-use/) | [[Forbes]] | | 6/30/2024 | [**Gretel CEO Ali Golshan Explains Why Synthetic Data Is Better for AI - Business Insider**](https://www.businessinsider.com/big-tech-mistake-training-ai-on-messy-public-data-2024-6) | [[Business Insider]] | ## Topic Overview (Some LLM-derived content — please confirm with above primary sources) ### Key Players - **YData**: A vendor specializing in synthetic data solutions, addressing the challenges of data scarcity and privacy compliance. - **Meta**: A technology company that invests in AI research and development, including the use of synthetic data for training models. - **Databricks**: Introduced synthetic data capabilities to assist developers in evaluating AI agents, enhancing performance metrics significantly. - **Gartner**: A research firm that identifies synthetic data as a key trend for 2025, advocating for its use to fill gaps in data insights and protect sensitive information. - **Advex**: Developing synthetic data generation technologies to enhance AI model production and performance in industrial applications. - **SmartOne.ai**: A Montreal-based company that provides synthetic data solutions to enhance AI model training with high precision, realism, and scalability. - **Medidata**: A brand of Dassault Systèmes that utilizes synthetic data to simulate patient cohorts, improving clinical trial performance and patient experiences. - **OpenAI**: An AI research organization focused on developing advanced models that utilize synthetic data for various applications. - **Gretel**: A company specializing in synthetic data solutions, advocating for its use in AI development to enhance privacy and reduce bias. - **MDClone**: A technology provider that specializes in synthetic data generation, enabling healthcare organizations to create data that preserves patient confidentiality while providing valuable insights. - **SAS**: A leader in analytics and data management, SAS has acquired Hazy to enhance its synthetic data generation capabilities. - **Duality AI**: A company providing a digital twin simulation platform that helps developers learn to build AI models using synthetic data. - **Ali Golshan**: CEO of Gretel, promoting synthetic data as a superior alternative to public data for AI training. - **Google**: A major player in AI and machine learning, utilizing synthetic data to enhance its AI models and applications. - **Rockfish**: A startup from Carnegie Mellon University that creates synthetic data solutions to address operational workflow challenges and data silos. ### Partnerships and Collaborations - **HCLTech and Life Sciences Firms**: Working together to develop AI-driven platforms that utilize synthetic data for operational improvements. - **MDClone and healthcare organizations**: Working with major health systems to leverage synthetic data for improving patient outcomes and research. - **Google and Research Institutions**: Partnering to explore the use of synthetic data in rare diseases and oncology. - **Nvidia and Healthcare Providers**: Collaborating to enhance AI model accuracy and efficiency using synthetic data. - **MDClone's ADAMS Center**: MDClone's ADAMS Center empowers healthcare staff to engage with data, promoting innovation through synthetic data generation. - **Gretel Collaborations**: Gretel collaborates with major companies across various sectors, including healthcare and gaming, to facilitate the use of synthetic data for AI development. - **Rockfish with AWS and Azure**: Partnerships to integrate synthetic data solutions with major database providers. - **DataMesh and NVIDIA**: DataMesh, a digital twin company, collaborates with NVIDIA to enhance data integration and simulation capabilities through its FactVerse platform. - **Google Cloud and Swift**: Collaborating to create a secure solution for financial institutions using federated learning and synthetic data. - **King's College London and University College London**: Developed an AI model that generates realistic synthetic images of the human brain for medical research. - **Databricks and MosaicML**: Integration of technology to provide comprehensive tools for building and evaluating AI solutions. - **Medidata and major pharmaceutical firms**: Medidata has expanded its customer base significantly, partnering with major pharmaceutical companies to drive breakthroughs in research and support ongoing patient care. - **NEC and Aetion**: This partnership aims to leverage real-world data from Japan to improve decision-making in healthcare and drug development globally. - **Brightline Interactive and U.S. Army**: Brightline has signed a Cooperative Research and Development Agreement with the U.S. Army to enhance synthetic imagery for AI training. - **Maverick Medical AI and RadNet**: Collaboration for deploying mCoder and integrating the CodePilot tool to enhance coding workflows. - **McGill University Health Centre (MUHC) and MDClone**: The ADAMS Centre at MUHC integrates MDClone technology to enhance data analysis for improved patient care and decision-making. - **Mayo Clinic**: Collaborated on refining algorithms for early detection of cardiovascular events, addressing biases in medical technology. - **Verizon and NVIDIA**: Partnering to launch a solution integrating 5G private networks and AI software, aimed at delivering secure, low-latency AI-powered services. - **Lambda and Nous Research**: Lambda is expanding its partnership with Nous Research to foster innovative research through its Researchers-in-Residence program. ### Innovations, Trends, and Initiatives - **Synthetic Data Adoption**: A growing acceptance of synthetic data in healthcare and life sciences to address data scarcity and privacy issues, with many organizations already utilizing or considering its use. - **Synthetic Data Generation**: A growing market projected to reach $3.7 billion by 2030, driven by the need for high-quality data in AI training and privacy concerns. - **Synthetic Data Generation Techniques**: Includes fake or rule-based generation, simulations, and data-driven generation through generative models. - **Synthetic Data Market Growth**: The synthetic data generation market is projected to grow from USD 0.29 billion in 2023 to approximately USD 3.79 billion by 2032, with a CAGR of 33.05%. - **Synthetic Data Generation Algorithms**: New algorithms like MIIC-SDG are being developed to generate synthetic data from electronic health records, improving quality and privacy metrics. - **Synthetic Data for Medical Imaging**: Using synthetic images to train AI models while addressing privacy concerns associated with real patient data. - **Generative AI in Healthcare**: Transforming drug discovery, clinical trials, and personalized medicine through the creation of synthetic data. - **Generative AI Growth**: The rise of generative AI is leading to increased applications of synthetic data, with predictions that 60% of AI data will be synthetic by 2024. - **Advancements in AI Models**: Research indicates that synthetic data can enhance AI model training, but reliance on it without real data can lead to issues like 'Model Autophagy Disorder' (MAD). - **Gretel's Generative AI-Powered System**: Allows users to create synthetic datasets for tabular data using natural language prompts, enhancing privacy in AI training. - **HarmonAIze by Toluna**: A synthetic data suite designed to enhance market research, enabling faster analysis without extensive real-world data collection. - **SYNTA Method**: A new approach for generating photo-realistic synthetic biomedical images, validated in muscle histopathology, enabling expert-level segmentation using synthetic training data. - **Deep Learning Techniques**: Innovative methods like Denoising Diffusion Probabilistic Models (DDPM) are being used to generate synthetic data for improved analysis in healthcare. - **Generative AI Techniques**: Techniques like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are being used to create synthetic datasets that enhance AI model training and improve diagnostic accuracy. - **Generative Adversarial Networks (GANs)**: GANs are being effectively used to create synthetic tabular healthcare data, streamlining the development of data-driven healthcare applications. - **Market Growth**: The AI training dataset market is projected to grow significantly, driven by the demand for diverse and advanced data, including synthetic datasets. ### Challenges and Concerns - **Quality of Synthetic Data**: Ensuring that synthetic data accurately mimics real-world data to maintain the effectiveness of AI models. - **Data Scarcity**: Synthetic data is essential in fields like healthcare where real data is scarce or inaccessible due to regulations. - **Over-Reliance on Synthetic Data**: Experts caution against over-reliance on synthetic data due to potential risks, including malicious inference of sensitive information. - **Data Privacy and Compliance**: Data privacy regulations pose challenges for organizations, making synthetic data a compelling alternative to traditional data collection methods. - **Bias in Synthetic Data**: The use of synthetic data in studies may introduce bias, raising concerns about the validity of AI models developed using such data. - **Overreliance on Synthetic Data**: Research warns that excessive dependence on synthetic data can lead to model degradation and loss of quality in AI outputs. - **Data Quality**: Ensuring the quality and representativeness of synthetic data is crucial for effective AI model training and clinical applications. - **Ethical Integration of Synthetic Data**: The need for ethical integration of synthetic data technologies to ensure compliance with data protection regulations and to foster continuous improvement in healthcare practices. - **Data Privacy**: While synthetic data enhances privacy, there are ongoing concerns about the ethical implications and the need for robust governance in AI applications. - **Privacy and Ethical Concerns**: While synthetic data offers privacy advantages, there are ongoing concerns about data misuse and the need for responsible data handling. - **Privacy and Compliance**: The use of synthetic data raises concerns about privacy, bias, accuracy, and accountability, necessitating a standardized governance framework. - **Regulatory Compliance**: Concerns regarding data privacy and compliance regulations that impact the deployment of synthetic data solutions. - **Quality Assurance**: The need for rigorous validation processes to ensure the quality and reliability of synthetic data is critical. - **Bias Propagation**: Concerns about biases in synthetic datasets affecting AI models, necessitating responsible use and guidelines for synthetic data generation. - **Legal and Regulatory Compliance**: Organizations must ensure compliance with regulations like GDPR and HIPAA when using synthetic data for research. - **Privacy and Ethical Considerations**: Ongoing evaluations are necessary to ensure synthetic data remains a secure tool for advancing medical research without compromising individual privacy. - **Model Collapse**: Overreliance on synthetic data may lead to model degradation and biases, raising concerns about the quality of AI outputs. - **Regulatory Hurdles**: Challenges in navigating the regulatory landscape for AI and synthetic data applications in healthcare.