From AI Methods to Clinical-Trial Transformation: Ten Years of Research at the van der Schaar Lab Research

A collection of our research, publications and resources exploring how AI is transforming clinical trials.

Overview

Clinical trials are essential for bringing new treatments to patients, but they remain one of the most challenging stages of medical innovation. They are often slow, expensive and operationally complex, making it difficult to identify early which patients are most likely to benefit, which may be harmed, and which trial designs are most likely to succeed. The problem is not simply that trials need to be faster. The deeper challenge is that clinical development remains limited in its ability to continuously learn and adapt from accumulating evidence.

For more than a decade, the van der Schaar Lab has been developing the machine learning foundations needed to change this. Our work has spanned causal AI, digital twins, synthetic data, adaptive clinical trials, AI for pharmacology, individualized treatment-effect estimation, and, more recently, agentic AI systems for clinical development. Together, these advances contribute to a new generation of AI-enabled clinical trials that are more adaptive, personalised and efficient.

This page brings together a selection of our publications, articles and resources exploring how advances in AI and machine learning can transform clinical development, accelerate the delivery of better treatments to patients, and help shape the future of medicine.

Causal AI

At the centre of this vision is causal AI. Clinical trials are not only about predicting outcomes, but they are also about making better decisions. What would happen if we changed the dose, the eligibility criteria, the comparator, the endpoint, the site mix, or the treatment strategy? Which patients are likely to benefit, and under what assumptions? What evidence is needed before a decision can be trusted?

The van der Schaar Lab has pioneered methods for causal effect inference and individualized treatment-effect estimation that help answer these questions. Our work enables researchers to better understand how treatments affect different patients, predict the consequences of interventions and generate evidence that can support more informed clinical and regulatory decisions.  

Digital Twins

This causal foundation naturally leads to digital twins. In our vision, digital twins are not generic simulators or decorative AI tools. They are dynamic, adaptive, causally grounded models of patients, diseases, populations, trials and healthcare systems. They allow us to ask: what could happen before we run the trial? Which assumptions are fragile? Which patients should be enrolled? How might disease trajectories evolve? How should we adapt if new evidence emerges?

The lab’s work on digital twins provides a route to stress-test trials before launch, support in-silico evidence generation, and support more informed clinical development decisions. Together, these technologies offer a path towards clinical trials that can continuously learn, adapt and improve over time.

Synthetic Data

A third major pillar is synthetic data. Clinical development is constrained by limited access to high-quality, representative, privacy-preserving datasets. Over many years, the van der Schaar Lab has developed methods and tools for generating, evaluating and applying synthetic healthcare data to help address these challenges.

Synthetic data can support privacy-preserving data access, data augmentation, fairness, benchmarking and machine learning model development, while enabling researchers to work with realistic healthcare data in a safe and scalable way. This work includes open-source software such as SynthCity and more recent work on making synthetic data generation accessible to clinicians and healthcare researchers.

Adaptive Clinical Trials

The lab has also been at the forefront of adaptive clinical trials. Traditional trials often rely on decisions and assumptions made at the outset, with limited ability to respond to new information as it emerges. Adaptive trials offer a different paradigm: learning during the trial while preserving scientific validity, transparency and trust.

Over the years, our research has developed machine learning approaches that can support better patient allocation, cohort enrichment, treatment adaptation, and decision-making under uncertainty. The goal is not uncontrolled flexibility, but the ability to adapt when the evidence supports it, helping clinical trials become more efficient, informative and responsive.

AI for Pharmacology

Another key area of research is AI for pharmacology and pharmacometrics. Clinical development depends on understanding dose, response, toxicity, pharmacokinetics, pharmacodynamics, disease progression and patient heterogeneity. The van der Schaar Lab’s work combines mechanistic modelling with modern machine learning to improve prediction of drug response, enable precision dosing, learn dynamical systems, and connect pharmacological theory with clinical data. By connecting pharmacological theory with clinical data, this work aims to support not only more efficient clinical trials, but also the scientific and biological questions at the heart of drug development.

Agentic AI for clinical trials

Most recently, the lab has been developing agentic AI for clinical trials. Bringing a new treatment to patients requires decisions across many areas, including biology, statistics, operations, regulation, safety, recruitment, and real-world evidence. As the volume and complexity of information continue to grow, there is an increasing interest in how AI systems can help researchers make sense of that evidence and support better decision-making.

In our vision, AI agents can become active reasoning partners: monitoring evidence, identifying fragile assumptions, stress-testing designs, integrating data streams, detecting drift, and helping teams ask better questions before failures occur. In our vision, these agents do not replace clinical, statistical or regulatory expertise. Rather they augment it, helping to create a new layer of clinical-development intelligence.

The opportunity now is to bring these advances together. Clinical trials are not transformed by any single technology, but by combining complementary approaches that address different parts of the development process. Causal AI helps answer the right questions, digital twins enable simulation and scenario testing, synthetic data expands access to information while protecting privacy, adaptive methodologies support better decision-making during trials and pharmacological AI connects mechanisms and treatment response, and AI agents helps researchers navigate increasingly complex evidence, assumptions and decisions across the development lifecycle.

Together, these technologies offer a new foundation for transforming clinical trials from isolated, rigid studies into continuously learning evidence systems. Before a trial begins, we can stress-test its design. During the trial, we can monitor assumptions and adapt responsibly. After the trial, we can feed the evidence back into digital twins, causal models, synthetic-data engines and agentic systems that improve the next trial.

The van der Schaar Lab has spent the past decade developing the scientific foundations for this transformation. The challenge now is translation: building validated, auditable, regulator-aligned tools that can be used in real clinical-development settings. If successful, this could make trials faster, safer, more predictive, more efficient, and ultimately better able to bring the right treatments to the right patients sooner.

This page brings together a selection of the publications, articles, software tools and resources that contribute to this vision of AI-enabled clinical development.