Alireza Moayyeri
Digital Excellence Lead, Global HTA & Payers UCB
Dr. Alireza Moayyeri, a senior biopharmaceutical executive and physician-scientist, leverages over two decades of experience at the intersection of clinical medicine, epidemiology, and digital transformation. As Senior Director and Digital Lead for Real-World Evidence (RWE) at UCB, he is recognised for pioneering the shift from traditional data analysis to dynamic, AI-driven insights. Known as a visionary architect, Dr. Moayyeri specialises in operationalising Agentic AI and Federated Data Networks to address industry challenges such as data sovereignty, regulatory velocity, and cross-border evidence synthesis.
Seminars
This hands-on workshop moves beyond AI theory and puts attendees directly into the RWE study lifecycle. Working through practical use cases, participants will explore how AI can be applied across protocol development, statistical planning, data abstraction and evidence generation, while navigating the validation, confidentiality and regulatory challenges that come with implementation. By the end of the session, attendees will have practical prompts, tools and frameworks they can take back to their teams and apply to their own RWE programmes.
Attend this workshop to:
- Work through the RWE study lifecycle and identify where AI can meaningfully accelerate processes without compromising scientific rigour or regulatory requirements.
- Develop and test practical AI prompts and workflows for protocol development, statistical analysis planning and medical record abstraction.
- Build approaches for validating AI-generated outputs, establishing appropriate gold standards and maintaining human oversight before evidence reaches regulatory submission
- Debate whether greater European data connectivity will genuinely enable cross-country RWE or simply shift the challenge towards agreeing which data and populations are sufficiently comparable
- Explore how increasing access to cross-country data could affect evidence expectations for smaller European markets and country-specific treatment patterns
- Discuss whether EHDS can help pharma move beyond fragmented national datasets while balancing data privacy, ethical requirements and scientific relevance
- Establish practical frameworks for validating AI tools, assessing bias and defining gold-standard measures for AI-generated outputs
- Navigate the regulatory and compliance challenges of implementing AI in highly regulated pharmaceutical environments
- Develop governance structures, transparency requirements and human oversight models that enable responsible AI adoption across RWE teams
