The Power of Digital Twins in Predicting Outcomes for Clinical Trials

In a recent interview with Applied Clinical Trials, Jon Walsh, founder and chief scientific officer of Unlearn, discussed how AI-designed therapies and digital twin technologies are reshaping the clinical trial landscape. While the fundamental principles of clinical testing remain consistent, Walsh explained that AI-discovered drugs often warrant deeper attention to specific biomarkers and safety signals in early development. He also highlighted how digital twins—virtual models of trial participants—can significantly improve trial efficiency, reduce control group sizes, and enhance the precision of treatment effect estimates. As regulatory agencies like the FDA and EMA begin to offer clear frameworks for integrating AI into drug development, Walsh emphasized the importance of transparency, reproducibility, and interpretability in model design and implementation.

ACT: Can you explain how digital twins work in the context of clinical trials and what makes them especially suited for evaluating AI-discovered drugs?

Walsh: Digital twins is a term borrowed from engineering. In that field, you might have a digital twin of a bridge or a jet engine to understand how the physical object changes over time, and how to maintain or improve it.

In clinical trials, we apply the same concept to individual patients. A digital twin is a model of what’s expected to happen to a patient if they’re receiving standard of care or a specific therapy. It predicts their outcomes probabilistically and models the patient comprehensively over time.

For every patient in your trial, you create a digital twin that simulates what would happen to them under standard of care. This doesn’t mean adding new patients to the trial—it means gaining more insight into the patients already enrolled. Think of a digital twin not as a new participant, but as additional data about each existing participant.

In a randomized trial, your ability to detect efficacy depends on how precisely you can estimate the treatment effect—that is, the difference between the treatment and control arms. Digital twins help here by providing more detailed information about each participant, which can be used in statistical analyses to refine the estimate of treatment effect. That increases statistical power and allows sponsors to run trials more confidently—and potentially with fewer participants.

In early-phase trials or open-label studies, digital twins can also serve as a comparator arm. For example, if everyone in the study is receiving the experimental treatment, the digital twin can model the counterfactual—what would have happened if the patient were on standard of care. That allows you to estimate treatment effect even at the individual level, without having to enroll a separate control arm.

Full Interview Summary: AI-designed therapies can present unique characteristics in clinical trials compared to traditionally developed treatments, often requiring closer scrutiny of biomarker signals and safety data early on. However, the fundamental process of clinical testing remains consistent. Digital twin technology helps make these trials more efficient and insightful by modeling individual trial participants under standard of care, offering a virtual comparator without needing additional enrollees.

Digital twins work by creating a probabilistic model of how a specific patient would respond to standard treatments over time. Rather than enrolling new participants, sponsors gain deeper insights into existing ones, improving the precision of treatment effect estimates and boosting trial power. In randomized controlled trials, this added statistical confidence can reduce participant numbers or accelerate timelines. In open-label studies, digital twins can serve as virtual control arms, allowing meaningful comparisons when a real control group is not feasible.

Regulatory agencies like the FDA and EMA are providing clear guidance on the use of AI and digital twins in drug development. They emphasize defining the model’s context of use, evaluating its risks, and proving its reliability. The companies building digital twins must ensure model development is transparent, reproducible, and interpretable—tracking how models were built, and which data were used, while safeguarding against unblinding.

Looking ahead, digital twins are expected to become a foundational part of the clinical trial landscape, particularly in neuroscience and CNS trials. They offer the promise of more adaptive, efficient, and patient-centric designs that can accelerate drug development and reduce reliance on traditional control groups, while maintaining—or even improving—the quality and reliability of clinical evidence. As adoption grows, digital twins could reshape how sponsors design trials and regulators evaluate new therapies.