Patient perspectives have long informed clinical trial design, but AI now makes this process scalable. This session explores how AI-driven tools capture and analyze patient insights across larger populations, informing endpoint selection, digital protocols, and trial workflows. Panelists will share strategies for leveraging AI to enhance trial relevance, engagement, and outcomes while maintaining scientific rigor and regulatory compliance.

AI is reshaping clinical data departments, driving new workflows, faster insights, and greater efficiency. This session explores the change management challenges that come with AI adoption, including workflow redesign, staff adaptation, and maintaining data quality and compliance. Panelists will share practical strategies for navigating these transformations and successfully integrating AI into everyday operations.

AI is transforming clinical trials, but successful implementation requires careful planning and execution. This session explores best practices, strategies, and approaches for integrating AI into trial design, operations, and data management. Panelists will discuss practical lessons on workflow adaptation, team training, regulatory considerations, and measuring impact to ensure AI adoption drives efficiency, data quality, and meaningful clinical outcomes.

AI is enabling a new era of personalized mRNA oncology therapies, making patient-specific approaches scalable. This session explores how AI-driven tools inform target selection, optimize trial design, and accelerate development timelines. Panelists will discuss strategies for integrating AI into mRNA programs, balancing personalization with efficiency, and delivering precise, data-driven oncology treatments at scale while maintaining safety and regulatory compliance.

AI is rapidly transforming clinical development, and we are approaching a future where frontier AI models will outperform humans in complex reasoning tasks. To have the maximum impact on improving patient outcomes, AstraZeneca is applying AI across oncology R&D to help deliver on its bold ambition of one day eliminating cancer as a cause of death.

This presentation will share Moffitt Cancer Center’s journey in embedding AI into clinical trials operations, highlighting real-world use cases, implementation strategies, and measurable outcomes. Drawing from initiatives such as EHR-to-EDC integration, AI-enabled prescreening, and CPOE deployment, it will offer a practical roadmap for scalable AI adoption aligned with institutional goals.

AI is reshaping clinical trials, driving operational change and new efficiencies—but how do organizations manage this transformation and measure its value? This session explores strategies for implementing AI, tracking performance, and assessing ROI.

AI is transforming clinical trials, but its success depends on skilled, prepared teams. This session explores strategies for training clinical operations, data, and trial management teams to effectively use AI tools. Panelists will discuss best practices for upskilling staff, integrating AI into workflows, and ensuring data quality, compliance, and trial efficiency while fostering collaboration and adoption across teams.

A robust data framework is essential for effective AI implementation in clinical research. It ensures high-quality, interoperable, and secure data across diverse sources, enabling reliable model development and regulatory-grade insights. By integrating standardized data structures, governance, and validation processes, such a framework accelerates evidence generation, supports ethical AI use, and enhances the efficiency and impact of clinical research.