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 generation of synthetic data in healthcare has emerged as a promising solution to surmount longstanding challenges inherent in the use of real patient data. By replicating the underlying ...

Synthetic data is generated as a replacement for real data that is considered poor quality, fragmented, siloed, sensitive or otherwise unusable for AI training in the enterprise. However, synthetic ...

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 ...

Three years into the modern artificial intelligence revolution, data protection can no longer be an afterthought - it must be ...

Synthetic data is becoming an increasingly attractive tool for companies looking to accelerate their AI development. By simulating realistic scenarios, it can protect privacy, speed up model training ...

When federal researchers tried to build an AI model to detect fraud in disability claims, they ran into a roadblock: they couldn’t use real claimant data. The workaround wasn’t a weaker model — it was ...

Jēnna Reese is CEO of Connect Centric, a D.C.-based firm that helps Fortune 500s and large nonprofits execute technology initiatives. In the race to modernize with AI, a new kind of risk is quietly ...

COMMISSIONED: As with any emerging technology, implementing generative AI large language models (LLMs) isn’t easy and it’s totally fair to look side-eyed at anyone who suggests otherwise. From issues ...

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On November 7, CAAI hosted Dr. Ryan Kappedal, ’19, a Booth alumnus and Technical Lead Manager at Google, for an insightful discussion on the evolving landscape of AI and the critical role of data ...