In a time when health systems are struggling to gain meaningful insights from data – and simultaneously aware that safeguarding patient privacy is essential – synthetic data offers a lot of potential.

The first time synthetic data was used to mimic real-world data was in 1993 by Donald Rubin. He created data that was statistically like genuine data, but without the risk of privacy compromise. With ...

As AI scaling laws hit the data plateau, executives face a stark choice: hit the scaling wall or remain an innovator. This ...

Databricks Inc. today introduced an application programming interface that customers can use to generate synthetic data for their machine learning projects. The API is available in Mosaic AI Agent ...

While the datasets are useful tools for training AI models, they do come with their own risks, from regulatory risks to ...

The industry’s answer? Synthetic data. “Recently in the industry, synthetic data has been talked about a lot,” said Sebastien Bubeck, a member of technical staff at OpenAI, in the company’s ...

As more companies invest in generative AI (gen AI) for bespoke use cases and products, proprietary data is becoming increasingly important to training large language models (LLMs). Unlike ChatGPT, ...

Reasoning Models for Text Mining in Oncology: A Comparison Between o1 Preview, GPT-4o, and GPT-5 at Different Reasoning Levels A data set of 1052 patients with human epidermal growth factor receptor 2 ...

Accessing PHI for development and testing is often blocked by stringent HIPAA compliance requirements. Learn how synthetic data helps engineers build tools to close care gaps and improve HEDIS scores.

Data is the life-blood of physical AI. Collecting real-life data is expensive. Generative AI and diffusion to create ...

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