Enhancing Clinical Trial Integrity Through AI and Digital Twin Technologies

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: How do you ensure transparency and reproducibility when using AI and digital twins in clinical research?

Walsh: One of the key components of the FDA’s seven-star framework is the ability to evaluate and demonstrate that a model appropriately addresses risks and is aligned with its context of use. In practice, this means that when you build the underlying machine learning models that power digital twins, and when you apply them in studies, the development process must be traceable.

You need to fully understand the data that goes into building the model, how the model was developed, and the decisions made throughout that process. Then, when generating digital twins for a trial, you must be able to demonstrate that this generation occurs in a trustworthy, pre-specified manner. For example, the data must exist ahead of unblinding and cannot be used by sponsors to unblind their own trial data. By satisfying certain criteria around the chain of custody and generation of data, you can trust the results of the analysis.

We have worked hard to build software and systems that enable this level of transparency. Another critical component is interpretability. Regulators are rightly concerned about understanding how predictions are made by AI models in drug development. Applying interpretability tools to digital twins—especially those derived from complex models—helps identify which variables are most important, what drives disease progression, and whether the outputs make sense in the overall clinical context.

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.