Session (CTI)

Purpose: Generative AI (GenAI), particularly agentic AI systems, is rapidly transforming HEOR. While early applications suggest substantial productivity gains, the extent of these benefits and their implications for scientific rigor remain unclear. At the same time, evidence requirements are becoming increasingly complex, requiring HEOR teams to generate high-quality insights faster than ever. This panel will explore how productivity in HEOR should be redefined beyond time and cost savings to include evidence quality, decision impact, and patient outcomes. Panelists will discuss how AI can enhance speed, scale, and scientific excellence while maintaining methodological standards expected by HTA agencies. Description: Moderated by Uwe Siebert (past ISPOR President), the session will begin with an overview of recent advances in GenAI and their implications for HEOR workflows, evidence generation, and quality assurance. Jag Chhatwal will present academic applications of GenAI in health economic modeling, including AI-driven model verification conducted with NICE and full replication of a published ICER model. These examples demonstrate how tasks traditionally requiring weeks can be completed in hours while preserving transparency and methodological rigor. Turgay Ayer will provide an AI developer perspective, showcasing a large-scale agentic AI system comprising more than 1,000 specialized agents. He will demonstrate its use in producing comprehensive landscape assessment reports within 48 hours and discuss how multi-agent architectures can coordinate evidence identification, synthesis, and reporting while minimizing hallucinations. Ipek Stillman will offer an industry perspective on GenAI-driven efficiency gains, examining whether productivity improvements can translate into meaningful organizational value. She will discuss implementation challenges and propose how agentic AI can help HEOR evolve from an evidence-generation function to one focused on enabling better decisions. The session will conclude with a discussion on how regulators, HTA agencies, and other stakeholders may redefine expectations as AI adoption accelerates.