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On September 23, 2026, MadeAi hosted a panel discussion bringing together experts across evidence generation, HEOR, market access, and AI to examine how AI should be evaluated in real-world evidence generation workflows.
The session explores the growing gap between traditional AI evaluation frameworks and the complex systems being deployed today, where performance depends not only on underlying language models, but also on architecture, validation processes, human oversight, traceability, governance, and compliance.
Designed for HEOR, evidence generation, market access, and life sciences professionals, the discussion offers practical perspectives on where current frameworks fall short and what fit-for-purpose evaluation standards could look like in practice.
Whether you’re evaluating AI vendors, developing internal AI standards, or implementing AI within evidence workflows, this session provides timely perspectives on building more rigorous and relevant approaches to AI evaluation.
Meet the Presenters
Dr. Ṣẹ̀yẹ Abogunrin MB BS, MPH, MSc
Global Access Evidence Lead, Roche
Dr. Ṣẹ̀yẹ Abogunrin brings expertise in evidence generation, health technology assessment, and AI-enabled evidence synthesis. His work explores large language models for systematic literature reviews and evidence screening.
Manuel Cossio MMed, MEng
Head of AI Solutions, RWE, Value & Access, Cytel
Manuel Cossio brings more than a decade of experience in healthcare AI research and development. His work focuses on generative and agentic AI, data governance, and human-in-the-loop approaches for reliable evidence generation.
Ramiro Gilardino MD, MHS, MSc
Science & Data Policy Advisor; Partner, Insights & Impact
Dr. Ramiro Gilardino brings more than 15 years of experience across biopharmaceuticals, consulting, and global health policy. His expertise spans pricing, reimbursement, health technology assessment, and market access strategy.
Angeline Dhas
Head of Product, MadeAi
Angeline Dhas leads product strategy for MadeAi, with more than 12 years of experience across healthcare and evidence generation. Her work focuses on AI-enabled solutions that support HEOR, literature review, and evidence workflows.
