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Title: From Point Solutions to Connected Workflows: Building Trustworthy AI for Real-World Evidence
Thursday, January 28, 2027
1:00PM EST | 6:00PM UTC | 7:00PM CET
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Description
Agentic AI has the potential to reshape real-world evidence (RWE) generation, shifting organizations from predominantly execution-based activities toward more systematic and scalable evidence generation for better decision-making across the product lifecycle. Today, task-oriented AI agents support discrete parts of the RWE generation process, such as code list development, feasibility assessment, literature review, protocol drafting, and programming. While these solutions may improve efficiency, organizations are increasingly thinking about whether connected agentic workflows can coordinate these individual tasks into integrated workflows, enabling more efficient and continuous RWE generation while preserving appropriate human oversight.
This evolution raises important questions. Which activities are best supported by general purpose LLMs and where do purpose-built RWE agents provide additional value? What capabilities, such as having fit-for-purpose data, are required to support scientifically rigorous evidence generation rather than simply accelerating specific tasks? How should AI-generated outputs be evaluated and governed? What roles will human experts continue to play in study design, interpretation, and decision-making? And how will AI-enabled workflows reshape existing workflows and SOPs?
This webinar will bring together leaders from life sciences organizations that are implementing AI for RWE. Each speaker will discuss a use case showing how AI is being applied, evaluation of its performance and quality, and how its outputs are reviewed, refined, and incorporated into broader evidence generation workflows. The panel will explore organizational decisions that accompany AI adoption, including when task-specific agents may be sufficient, when more coordinated agentic platforms may be more valuable, how human expertise and governance should be incorporated, and what these changes mean for the future role of RWE teams. The discussion will highlight lessons learned and explore how collaboration between RWE experts and technology developers is informing the design of AI-enabled ecosystems that combine robust real-world data, RWE expertise, and intelligent workflows to support rigorous RWE generation.
Learning Objectives:
- Evaluate the foundational capabilities required for trustworthy AI-enabled RWE generation, including fit-for-purpose real-world data, scientific expertise, governance, and connected workflows.
- Assess lessons learned from early AI adoption in RWE and identify considerations for building AI-enabled evidence generation capabilities within one’s own organization.
Moderator:
Katie Mues, PhD, MPH, Head of Product Science, Datavant, Life Sciences, New York, NY, USA
Speakers:
Darmendra Ramcharran, PhD, Sr. Director, Safety and Quantitative Innovation, GSK, Providence, RI, USA
Manuel Cossio, MMed, MEng, Head of AI Solutions, RWE, Value & Access, Cytel Inc., Geneva, Switzerland
Sponsored by Corporate Partner, Datavant.
Please note: On the day of the scheduled webinar, the first 1000 registered participants will be accepted into the webinar. For those who are unable to attend, or would like to review the webinar at a later date, the full-length webinar recording will be made available at the ISPOR Educational Webinar Series webpage approximately 2 days after the scheduled Webinar. Reservations are on a first-come, first-served basis.
