This talk evaluates how generative AI, multimodal models, and agentic AI are moving from research prototypes into clinical research and trial operations. Key topics include drug development, site selection, RWE/RWD, patient identification, evidence generation, and workflow automation. The talk discusses practical use cases, frameworks, and tools for AI validation, explainability, uncertainty, and trustworthy deployment. The session closes with regulatory expectations and responsible adoption of generative AI  in clinical development.

AI implementation in clinical research must move beyond experimentation to sustained, measurable impact. This presentation offers a practical journey from identifying high-value ideas and aligning scientific and business priorities to funding, building, and deploying fit-for-purpose AI tools in real workflows. It addresses validation, governance, change management, and value measurement, while emphasizing the critical success factor: adoption. Attendees will gain a pragmatic framework for creating trusted AI solutions that are used, scaled, and sustained.

While the industry rapidly adopts AI to optimize trial design and execution, the greatest hurdle is no longer technological—it is behavioral. Drawing on over 27 years of experience navigating landscape shifts, this session explores the critical intersection of AI implementation, change management, and behavioral science. I will share strategic insights from the past 2 years leading AI transformation. It will detail how to move beyond pilot programs by building an internal "Community of Practice", upskilling clinical teams, and utilizing behavioral science principles to overcome resistance.

Artificial intelligence (AI) is reshaping how evidence is generated, assessed, and reviewed in clinical research. This presentation will explore the regulatory environment needed to enable AI-enabled evidence generation and the growing use of AI by regulators to support regulatory activities and review. It will consider near-term opportunities for AI-assisted approaches, the role of human oversight and accountability, and the need for fit-for-purpose, risk-based frameworks and greater global regulatory convergence.

Designing and running a clinical trial today means coordinating hundreds of interdependent decisions such as eligibility criteria, site selection, visit schedules, data flows, patient funnel, external feedback, etc. We'll share how our organization is using AI to model these decisions together, generating optimized protocols and orchestrating downstream execution in near real time. This session covers the architecture behind the approach, lessons from early deployments, and what it means for cross-functional collaboration.

Clinical operations teams rely on validated dashboards, but many study-specific questions still require manual joins, spreadsheet work, and interpretation across different levels of data. This presentation shares how an agentic AI analyst was designed to answer natural-language clinical trial operations questions. The session will demonstrate how AI can turn operational metrics into faster, traceable decision support.

Determining which documents a clinical trial needs for ethics and regulatory submissions by country is slow, manual, and error-prone. The Keystone Project predicts a list of documents by pairing curated regulatory intelligence with AI-driven confidence scoring using probabilistic and rules-based machine learning. This application gives study teams instant, auditable answers across 80+ countries and 125 documents. This talk covers how Keystone was built, validated, and its impact in study startup.

Enrollment forecasting is a foundational but under-specified problem in clinical trial design, typically addressed through proprietary, closed-form tools that offer little insight into their underlying assumptions. Trial Planner, developed at Regeneron, formalizes enrollment projection as a discrete-event simulation governed by a hierarchical Gamma-Poisson process, embedded within an outer Monte Carlo framework to propagate parameter uncertainty. All parameters are estimated via Empirical Bayes, drawing on multi-source real-world data including operational trial history, EHR-derived prevalence, and competitor-trial activity. Preliminary evaluation demonstrates improved predictive accuracy relative to an established comparator method, with forecast calibration improving further as on-trial enrollment data accrue.

The Clinical Research Associate (CRA) role remains highly manual and fragmented, and completion of time-intensive administrative tasks often surpasses the CRAs capacity for high-value activities such as site engagement, relationship-building, patient recruitment and retention support, and inspection readiness. To address this challenge, we are implementing an agentic AI virtual assistant designed to support CRA workflows.

Regeneron will share how SOP Assistant streamlines GxP Procedural Document change management through an innovative, end-to-end automated solution powered by Agentic AI. This streamlined automation supported business process reduces manual effort by approximately 50%, accelerates Global Development decision-making, and enables faster identification of future agency change impacts. Cross-functional collaboration, strong governance, product-focused delivery, and continuous improvement have shaped the vision of a true SOP Assistant.

This session will share Mayo Clinic’s approach to reimagining clinical trial start-up through agentically assisted site workflows. The presentation will highlight how protocol and study document data can be ingested once, routed downstream, and used to trigger timely operational reviews, reducing redundant data entry and handoffs while keeping human expertise focused on critical decisions that improve speed, consistency, compliance, and scalability, ultimately bringing cures to patients faster.

From the New York Times Games app to Mario Party, the gaming industry has mastered the art of keeping users engaged. Features like achievements, badges, leaderboards, and unlockable content motivate users to return again and again. Clinical operations teams increasingly rely on dashboards, control towers, and reporting platforms to support drug development, but how can we make these tools more engaging? This session explores gamification and the psychology behind successful games, sharing practical lessons that attendees can apply to drive adoption, engagement, and better user experiences.

A natural CLARA sequel from 2026 covering additional capabilities and user experience. Features to highlight will be MSTeams integration, industry benchmarking, new datasets, enrollment prediction, alerts, and next-best-action recommendations. We will also highlight the governance model implemented to ensure trust & accuracy. We would also cover some themes of “Humans in the loop with AI” which will speak to how we’re managing downstream user experience with our tool.

Clinical trial planning generates thousands of decisions across emails, meetings, presentations, and trackers, yet much of the rationale behind those decisions is difficult to reconstruct later. This presentation will showcase an AI-powered approach developed through a collaboration between Boehringer Ingelheim and Microsoft to automatically capture project change history ("churn"), create structured decision summaries, and establish a foundation for future predictive analytics. Attendees will see how AI can transform fragmented project information into organizational knowledge.

This session reviews the results of a first-of-its-kind new study quantifying the return on investment (ROI) in, and the net financial value created by, an agentic AI study monitoring solution. Based on hard data drawn from industry benchmarks and actual experience, the Tufts CSDD research team quantified the financial value associated with efficiencies gained from improvements in on-site study monitoring capacity and workload, reductions in on-site monitor travel-related costs, as well as efficiencies gained from improvements in remote administrative monitoring activities.

AI is rapidly moving from experimentation to deployment across clinical research, yet many organizations still struggle to measure value in a credible and repeatable way. This presentation describes a practical framework for evaluating AI initiatives by linking operational improvements to business outcomes through acceleration, productivity, cost savings, and cost avoidance—covering establishing baselines, avoiding common ROI pitfalls, and creating scalable approaches for prioritizing and governing AI investments.

Making the best use of AI in clinical analytics requires more than setting up an MCP server. A strong governance strategy; a well-designed semantic layer; a flexible, dynamic user interface; a thoughtful approach to integration into user workflows; and a pre-established evaluation plan are all essential for any project's success. Providing the strategy and tools to show end users "what's in it for them" leads to faster uptake and impact.