Synthetic Data: A Real Fix for Clinical Trials?

The industry loves to celebrate synthetic data as a privacy solution—but where’s the real impact?

Let’s be clear: privacy in clinical trials is non-negotiable. With the GDPR and other global privacy laws tightening their grip, sponsors are desperate for alternatives to real-world patient data that don’t put them at regulatory risk. Enter synthetic data—billed as the magic bullet for compliance, innovation, and cross-border collaboration. But for all the hype, has synthetic data lived up to the promise?

Synthetic Data 101: Hype Meets Reality

Synthetic data isn’t new. It’s data generated by machine learning models trained on real-world datasets to recreate statistical patterns—without including any actual patient records. That’s the selling point. No real patients means no personal data. And if there’s no personal data, GDPR doesn’t apply… right?

Not so fast. The data may look anonymous, but experts are still debating whether advanced re-identification techniques could undermine its safety. And the GDPR? Silent on the matter. The result? Regulatory ambiguity. Sponsors are left walking a tightrope—hoping regulators won’t question the synthetic label too hard.

Cross-Border Compliance Without the Headache?

In theory, synthetic data is a game-changer for multinational trials. GDPR makes international data transfers painfully complex. Synthetic datasets, devoid of identifiers, promise to cut through that red tape. But here’s the catch: most regulatory bodies still treat synthetic data with caution. Without clear criteria or auditability standards, it’s hard to know if you’re building with bricks or sand.

So far, no major regulator has issued definitive guidance, and no headline drug approval has hinged on synthetic datasets alone. Until that happens, sponsors will keep asking: is this a privacy solution or just another compliance mirage?

The FDA: Cautiously Curious

Unlike the EMA, which has yet to make any concrete statements about synthetic data, the FDA is starting to explore the possibilities—particularly in medical device development and AI model training. The agency’s Center for Devices and Radiological Health (CDRH) has launched internal research projects to assess whether synthetic data can supplement real-world datasets in the development and validation of medical AI.

FDA Grand Rounds have also brought synthetic data into the regulatory spotlight, with sessions focused on its role in improving medical imaging AI. The message is clear: the FDA sees synthetic data as a promising tool—but one that needs rigorous validation. In the agency’s own words, the challenge lies in ensuring that synthetic datasets actually represent real-world variability and complexity. Without that, the utility collapses—and so does trust.

These steps show cautious optimism, but they fall short of regulatory endorsement. Synthetic data may support AI development, but we’re still far from seeing it serve as standalone evidence for drug approvals. The FDA is investigating. But it hasn’t committed.

Privacy Preserving—But At What Cost?

Yes, synthetic data minimizes breach risks. Yes, it aligns with principles like data minimization and purpose limitation. But here’s the uncomfortable truth: if your original data is biased or incomplete, your synthetic dataset will be too. Garbage in, garbage out—just dressed up in AI jargon.

And don’t expect synthetic data to be the answer for rare disease trials or precision medicine. It struggles with granularity. In trials where every data point matters, synthetic replication simply can’t deliver the nuance needed for rigorous science.

The Industry’s Favorite New Buzzword

Let’s be honest—synthetic data has become the new buzzword for privacy-respecting innovation. Slide decks love it. Keynote speeches celebrate it. But measurable outcomes? Still sparse. Where are the regulators signing off? Where are the trials that gained speed, efficiency, or scale because of synthetic data?

We keep hearing synthetic data is the future—but without case studies, regulatory backing, or real-world successes, it’s still just a theory. Until sponsors stop showcasing synthetic data as a futuristic panacea and start proving its ROI in real trials, this conversation will remain a glossy distraction.

Moving Past the Hype

Here’s the bottom line: synthetic data is a tool, not a solution. Used alongside strong privacy measures—pseudonymization, encryption, data minimization—it can add value. But used in isolation, it risks being little more than a compliance smokescreen.

If we want real progress, regulators need to clarify their stance. Sponsors need to publish results. And the industry needs to stop patting itself on the back for adopting synthetic data until we see tangible benefits—not just buzz.

Because until synthetic data proves it can deliver both scientific value and regulatory trust, it’s not a privacy breakthrough—it’s just a placeholder.