Carez AI is a synthetic data platform for medical imaging, designed to support AI development in life sciences. It allows teams to generate high-fidelity, regulator-ready synthetic datasets without using patient data, eliminating privacy risks and clinical delays. The platform enables rapid creation of rare or edge-case scenarios, demographic balancing, and iterative validation directly within model development pipelines.

Gretel is a synthetic data platform—now part of NVIDIA—that enables users to generate artificial datasets that preserve the statistical properties of real data without including any actual sensitive information. The platform supports training generative models, validating output with privacy and quality metrics, and producing scalable synthetic data on demand. Gretel’s APIs and tools (like Data Designer and Safe Synthetics) are designed for developers and data scientists working on AI model training, testing, and privacy-preserving analytics across industries, including healthcare.

MakeData.ai is a synthetic data generation platform specializing in healthcare datasets that replicate real patient encounters while maintaining privacy and regulatory compliance. It enables healthcare product teams, QA, developers, and sales professionals to generate realistic, contextualized synthetic data for use in testing, demos, and development. Outputs are available in standard formats like HL7 FHIR R4, including eICR documents for conditions like Hepatitis B/C, Salmonella, and COVID-19.

The MDClone ADAMS Platform is a self-service data analytics tool for healthcare organizations to explore data at scale. It enables users to engage with clinical and non-clinical data, create synthetic data for collaboration, and conduct data analysis without programming skills. The platform supports clinical research, operational improvements, and patient care through modules like Ask, Discover, Act, Measure, and Share.

MOSTLY AI provides a powerful open-source Synthetic Data SDK that enables the generation of high-fidelity, privacy-preserving synthetic data for AI/ML development, testing, and analytics. The SDK supports local or cloud-based model training for various data types—including tabular, time-series, and text—and allows conditional data generation, segment rebalancing, and fairness controls. Users can integrate with external data sources, export generators, and leverage quality reports for fidelity and privacy assurance.

Subsalt Generative Database is a synthetic data platform that creates HIPAA-compliant replicas of healthcare datasets for research, prototyping, and AI development. It enables data teams to bypass traditional compliance bottlenecks by generating de-identified, schema-preserving datasets that maintain statistical fidelity while ensuring patient privacy. Subsalt supports SQL-based querying, integrates with common data tools, and includes third-party Expert Determination to meet regulatory standards.

Syntegra generates synthetic healthcare data with statistically accurate, privacy-preserved patient records for healthcare applications, including research, clinical trials, and digital health.

Synthea is an open-source synthetic patient data generator. It simulates lifelike health records for synthetic patients based on publicly available clinical guidelines and statistical data, covering disease progression, interventions, and outcomes across a full lifecycle. The data generated is free from real patient information and is widely used for software testing, academic research, and validating healthcare analytics tools without privacy concerns.

Interoperability Institute’s Synthetic Data offering provides high-fidelity, HIPAA-compliant synthetic healthcare datasets designed for AI model development, software testing, and interoperability validation. These datasets are entirely artificial—generated using advanced statistical and generative techniques—to closely mimic real-world patient scenarios without containing any protected health information (PHI). Available in both off-the-shelf and customizable formats, the data enables risk-free research, training, and product development across the healthcare ecosystem.

Syntheticus is a generative AI-powered platform that produces high-quality, privacy-compliant synthetic data for use in AI training, software testing, and business intelligence. Designed to solve challenges around data scarcity, privacy regulations, and bias, Syntheticus allows users to generate artificial datasets that mimic real-world data while ensuring statistical validity and anonymity. The platform supports diverse environments (cloud, on-prem, edge) and integrates functional modules tailored to applications in healthcare, finance, and other regulated industries.

Syntho offers a synthetic data generation platform that is used in healthcare to create privacy-preserving, statistically accurate data for analytics, testing, AI model training, and research. It supports various healthcare data types, including EHRs, clinical trials, claims, and registries, and is designed to work with time-series and longitudinal data common in medical contexts. Syntho’s platform helps hospitals, pharmaceutical firms, and academic researchers share and analyze data without risking patient privacy.

Tonic.ai is a healthcare-focused data de-identification and synthetic data generation platform that enables safe and scalable use of patient data for software testing, AI model training, and analytics. It supports structured, semi-structured, and unstructured data—including HL7 FHIR and EMR formats—using tools like Tonic Structural and Tonic Textual to remove PHI while maintaining data utility. Tonic's platform facilitates privacy-preserving use cases such as LLM prompt redaction, digital twin simulations, and retrieval-augmented generation (RAG).

YData provides a data-centric AI development platform tailored for industries like healthcare and pharma, with a strong emphasis on synthetic data generation and profiling. Its platform, YData Fabric, helps healthcare organizations safely access and share patient data by generating privacy-preserving synthetic datasets that maintain the statistical integrity of the original data. Key use cases include data augmentation, de-biasing imbalanced datasets for clinical model development, and enabling compliant data sharing between institutions.