Rethinking clinical trials with synthetic data

Part of the “Synthetic Data in Healthcare” blog series by Aindo.

 

Clinical trials are the backbone of medical progress. But designing and executing them remains one of the most complex, costly, and time-consuming aspects of healthcare innovation. Synthetic data offers a new path forward that enhances trial efficiency, reduces patient burden, and accelerates the route from discovery to treatment.

The limits of traditional clinical trials

Randomized controlled trials (RCTs) remain the gold standard for evaluating the safety and efficacy of new treatments. Yet they have persistent challenges: enrolling patients is difficult, timelines stretch for years, and the ethical cost of placebo arms – especially in life-threatening conditions – can be hard to justify.

Recruitment alone is one of the greatest bottlenecks in clinical research. It accounts for around 30% of total trial costs, with each patient costing roughly $6,500 to enroll and $19,000 to replace. Researchers consistently cite recruitment and retention as their top challenges after securing funding. In the United Kingdom, nearly one-third of publicly funded trials face major enrollment difficulties that require extending timelines or revising study goals. Dropout rates of 25–30% are common, with some trials reporting losses of up to 70%. These challenges not only drive up costs but can also undermine data quality and, in some cases, cause trials to fail altogether.

A new alternative: Synthetic external control arms

Synthetic data provides a powerful, privacy-preserving alternative to traditional trial design. Using advanced generative AI, it creates statistically accurate replicas of real patient populations without including any identifiable personal data. These datasets can be used to construct synthetic external control arms – virtual patient cohorts that replicate real-world conditions and outcomes.

This approach allows researchers to:

• Reduce or eliminate the need for placebo groups, limiting patient exposure to less effective or inactive treatments.
• Shorten trial timelines and lower costs, by supplementing or replacing control-group recruitment.
• Create more inclusive and diverse trial designs, by enriching underrepresented populations or simulating rare conditions.
• Protect privacy and maintain analytical rigor, ensuring regulatory compliance under GDPR and the EU AI Act.

Real benefits, real applications

Synthetic control arms are already transforming how evidence is generated in oncology, hematology, and rare-disease research, where small patient populations make traditional recruitment nearly impossible. By complementing or substituting real-world data, synthetic datasets strengthen statistical power while maintaining the same analytical validity as traditional methods.

At Aindo, we help healthcare innovators generate synthetic datasets tailored to clinical trial objectives. Our technology enables hospitals, research institutions, and pharmaceutical companies to safely simulate, analyze, and share patient data – enhancing statistical power, accelerating approvals, and upholding the highest ethical standards.

Looking ahead

Synthetic data marks a turning point for clinical research. By making trials faster, safer, and more inclusive, it is redefining what’s possible in medical innovation. And this is just the beginning.

Ready to explore synthetic data for your clinical research?

Contact us to learn how Aindo can help you innovate with privacy-safe, high-quality data.