Addressing Data Privacy Concerns in Clinical Development with Synthetic Data

The benefits of implementing AI into the clinical trial process are promising—potentially mitigating prevalent concerns surrounding data privacy and protection. In traditional clinical trial settings, companies are understandably hesitant about sharing clinical trial data due to the fear of violating patient data privacy or any of the strict data protection laws. 

It's widely known and understood that clinical trial data is crucial to properly informing relevant insights about future clinical developments. Without this data, companies would have much less evidence to use to make key decisions. Despite the need for this rich data, the ability to share it is highly regulated and often limited. 

Leveraging synthetic data can help to address these common concerns and roadblocks regarding data privacy. While synthetic data is “synthesized” through generative AI algorithms, it's derived from real-world events. A proper synthetic dataset retains its fidelity and acts just as traditionally generated, real data would—without the risk of leaking personal information. In a clinical trial setting, it's especially important that there are safeguards in place to ensure the privacy of data, while also preserving the integrity of the trial data. 

Synthetic data does not contain the key identifiers that often prevent data from being shared—making it anonymous and de-identified—therefore allowing the data to be leveraged in more scenarios than otherwise would be permitted.

Medidata’s Simulants is an example of an AI-generated synthetic dataset that can be used to safeguard patient data and privacy in a clinical trial program. Simulants has the ability to use detailed patient-level data, which is known as personally identifiable information (PII), while maintaining privacy and integrity.

Sponsors can use a generative AI tool like Simulants in their clinical trial program to gain valuable insights that may have traditionally been more challenging to obtain. These insights can help to inform go/no-go decisions that can ultimately lead to a trial’s overall success or failure, determine the safety and efficacy of their drug, mitigate adverse events, and more quickly select sites and patients. 

Overall, synthetic data has the ability to address and solve some of the roadblocks caused by data privacy and protection concerns in the clinical trial landscape. This type of AI-generated data helps sponsors derive key insights about their clinical development—which without, would lead to slower, costlier trial programs because of the need to work around data limitations and the inability to access such crucial clinical trial data. 

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