As data complexity increases across R&D, organisations are rethinking how data strategies can better connect clinical, operational, and translational data to support decision-making. This session will discuss breaking silos through integrated data approaches, cross-functional collaboration, and evolving data management models to enable a more cohesive and efficient R&D ecosystem.

This presentation shares how AI is reshaping the use of omics, biomarker, and digital data in clinical trials. The discussion will focus on turning complex, high-volume datasets into actionable insights for study design, patient stratification, endpoint development, and operational decision-making, while addressing integration, scalability, and scientific interpretation across clinical research workflows.

Clinical trials generate vast amounts of high‑quality data with significant untapped potential to accelerate clinical development through greater reuse of data. This session explores how the industry is turning that potential into impact by enabling data to be combined and analyzed across companies through TransCelerate’s Clinical Data Sharing initiative. Through practical examples, speakers will highlight how shared control-arm data can support proxy endpoint in clinical trials, while also discussing the broader ways historical trial data (HTD) can inform study design and deepen understanding of disease and safety signals—ultimately reducing unnecessary patient burden. The session will also look ahead to emerging use cases and future opportunities to accelerate clinical development through collaboration.

This talk explores how AI-processed longitudinal sensor data is enabling next-generation digital endpoints in clinical trials. Using Parkinson’s disease as an example, it highlights how continuous real-world data can improve sensitivity, reduce variability, and support faster, more precise trial outcomes while reshaping how endpoints are defined and evaluated in clinical research.

As clinical trial data grows more complex, organisations often leap toward AI before securing the foundational elements required for long-term success. This session focuses on building strong data governance, aligned analytics practices, clear domain ownership, and a culture that embraces data-driven decision-making. With these foundations in place, organisations become equipped to responsibly and effectively explore AI capabilities in their future roadmap.

How process intelligence creates the transparency, data quality, and operational context needed to enable AI-driven automation in clinical trials—ultimately improving cycle time, process efficiency, and resource management.

This session presents a comprehensive framework for implementing managed, reusable data products in clinical development. Learn how to combine business purpose, semantic standards, technical delivery, and governance into AI-ready assets accessible through standardised interfaces. Discover practical approaches to data contracts, quality rules, lifecycle management, and regulatory compliance that enable consistent, automated decision-making across R&D.

ClinicalAIDEN is an agentic AI–powered decision support system that transforms how clinical trial feasibility and study design insights are generated, evaluated, and translated into strategic options. By orchestrating AI agents across data aggregation, analysis, and scenario evaluation, the system reduces manual effort, shortens insight cycles, and enables teams to explore multiple, evidence-backed development options earlier. This leads to faster, more robust, and better-aligned R&D decisions.

This study presents an AI-driven engine that automates drafting clinical site queries, ensuring quality and reducing redundancy. The system uses context-aware templates, semantic de-duplication, integrity checks, and recursive validation to produce accurate, consistent, and actionable queries. Results show improved query quality, faster responses, and enhanced data integrity, demonstrating a scalable solution for efficient, high-fidelity communication in clinical monitoring.

Role-based AI “Virtual Teams” can generate exploratory clinical analyses by coordinating analysis planning, statistical coding, and narrative summaries. Because generative models are non-deterministic, repeated executions can be treated as stochastic samples from a distribution of plausible analytical ideas. Specifically, we will describe how this AI workflow repeats its processes, compares results across multiple runs, and provides the end user with convergence and variability assessments to enable efficient human-in-the-loop validation and accountability.