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.

The clinical operations environment is becoming increasingly challenging in the execution of clinical studies. Industry-wide, the clinical trials landscape is becoming more saturated, leading to delays and growing execution risks across late-phase clinical study portfolios. This session will illustrate these challenges and demonstrate how Merck is mitigating risk through the use of data, technology, and process improvements to better serve patients.

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 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.

In this session, we will be speaking about our foundational work to reduce tech burden for clinical trial sites, the use of AI to boost productivity and operational excellence for our internal teams, and our roadmap for further AI integration to a frictionless Site Engagement.

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 adoption in Clinical Trials is stuck in low impact pilots. The real opportunity is not better tools, but a fundamentally different ecosystem. This talk explores how next gen technology can reshape protocols, site-selection, data flow and decision making across the ecosystem. Drawing from frontline experience, we will move beyond incremental pilots to what it actually takes to deliver faster, simpler and more scalable clinical development.

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.

As AI adoption accelerates in clinical research, sites face growing pressure to implement new tools while navigating unclear governance frameworks, limited training, and fragmented technologies. At the same time, sites are increasingly evaluated by opaque AI-driven algorithms for feasibility, selection, and performance. This presentation explores the site perspective on AI governance, operational integration, and trust—and why meaningful AI transformation in clinical trials depends on genuine partnership and successful implementation at the site level.

AI offers transformative potential for clinical development, but its adoption must be responsible, ethical, and compliant. This session explores best practices for integrating AI into trials, addressing data quality, patient safety, regulatory expectations, and organizational readiness. Panelists will share practical strategies for leveraging AI effectively while maintaining scientific rigor and ensuring trust across teams, regulators, and stakeholders.

As clinical research teams chase faster timelines and earlier submissions, dashboards alone are no longer enough. This session explores the agentification of life sciences—how agentic AI systems can actively orchestrate trial design, operations, and regulatory authoring to compress critical paths. We will examine where agents deliver real acceleration today, and the governance, oversight, and validation guardrails required to scale speed without compromising regulatory trust.

The “perfect” clinical operations vendor platform does not exist and probably never will. Even leading suites cover only ~60–80% of real-world needs once governance steps, audit evidence, edge cases, and post-acquisition system realities enter the room. Organizations typically respond by force-fitting the platform, and end up paying twice: Process debt (manual workarounds, shadow processes, spreadsheet sprawl), and integration debt (fragile point-to-point connections, duplicated data, inconsistent metrics). In this session, we propose a different operating model: Core Platforms + Niche Capability Vendors + a thin, reusable Microservices Layer. The outcome: fewer workarounds, more consistent data, and teams spending time on decisions that improve trial delivery and ultimately patient impact.