From Potential to Practical at DIA 2026: A Three-Part Series on Unlocking AI’s Role in Modern Clinical Trials

Part 1 – Simulation in Practice: How AI Is Transforming Trial Design

Artificial intelligence has become an integral part of the vision for clinical research and development. The next phase for the industry is translating the progress we’ve made into consistent, scalable and trusted impact. At the 2026 Drug Information Association global meeting, sessions explored what it takes to operationalize AI across the clinical development lifecycle. highlighting three connected priorities: designing better trials, generating reliable data and ensuring AI is deployed in a governed, trusted and scalable way.

This comprehensive three-part series explores how these elements come together—from transforming trial design through simulation, to structuring data for reliable insight and establishing the governance and validation needed to scale AI with confidence.

One of the most immediate areas of progress is trial design, where AI-enabled simulation is reshaping how studies are planned, tested and executed.

Reimagining trial design: from static protocols to predictive simulation 

Clinical development faces mounting complexity as protocol burden and competition for patients and sites intensify. Traditional approaches to trial design may no longer be sufficient, and the data underscores why.

Today, 77% of trials experience at least one protocol amendment, while 80% are delayed, primarily due to enrollment challenges.^1,2 At the same time, study start-up timelines have increased by approximately 30%, reflecting the growing operational burden placed on sponsors.³

AI is beginning to rework this equation.

The industry is moving toward a fundamentally different model: clinical development simulation. Rather than relying on static assumptions or retrospective benchmarks, large-scale AI models can simulate trial scenarios before they are executed. This allows clinical trial sponsors to evaluate trade-offs across efficacy, feasibility, cost and timelines in a dynamic, data-driven way.

As presented at DIA 2026, this shift changes how trials are designed and how development decisions are made. Study teams can explore a wider range of options, pressure-test design choices and better predict probability of trial success earlier in the development process. This reduces the need for protocol amendments, minimizes delays and improves trial outcomes. 

Life science models: unlocking new use cases 

Part of what makes this possible is the emergence of large-scale domain-specific foundation models trained on clinical trial data, real-world patient data and biomedical datasets. These life science models enable patient-level simulation, modeling individual trajectories and predicting events to inform clinical trial endpoints. This is unlocking a new set of practical use cases, including:

• Optimizing eligibility criteria to maximize treatment effectiveness and safety.
• Enabling more targeted trial strategies through the identification of risk factors, biomarkers and patient phenotypes.
• Accelerating clinical trials by leveraging statistical power to reduce enrollment requirements.

But the pressure to rethink trial design is also being driven by upstream success. AI-enabled drug discovery programs are showing significantly higher early-stage success rates, resulting in more compounds progressing into clinical development and increasing competition for patients, sites and endpoints.

This makes design a key differentiator scientifically, but also operationally and commercially. However, design is only one part of the equation. To fully realize these gains, execution must also evolve.

That is where agentic AI can go beyond isolated automation to orchestrate workflows across the clinical lifecycle. These systems can connect scientific, regulatory and operational data for faster and more proactive decision-making across the full study cycle. 

Putting this into practice means: 

• Accelerating generation of trial scenarios and feasibility strategies. 
• Automating document-heavy processes, such as informed consent and Trial Master File management. 
• Enabling continuous, real-time oversight of study performance. 

Together, these advances signal a move away from reactive trial management toward predictive, continuously optimized development. As promising as these capabilities are, their impact depends on how well data are generated, structured and used throughout the study lifecycle, which is a challenge that extends beyond design alone.

1. Tufts CSDD Impact Report. Mar/Apr 2023; 25.
2. Desai M. Recruitment and retention of participants in clinical studies: Critical issues and challenges. Perspect Clin Res. 2020 Apr-Jun;11(2):51-53.
3. Tufts CSDD Impact Report 2023; 25.

As the Vice President for Machine Learning, Raja is determined to change healthcare with the power of AI, leading the team to create new narratives that fully leverage AI’s potential to reshape the industry from R&D through to commercialization. Raja brings together a diverse set of technical and strategic capabilities, including Machine Learning, Deep Learning, Generative AI, product development, life sciences expertise and business consulting skills. His team combines data science, domain expertise and consulting skills to drive impactful change by applying AI to life sciences and healthcare decisions.