ISSUE: The emergence of generative AI heralds a transformative era for HEOR, offering many opportunities to enhance methodologies across the discipline. This technology promises to revolutionize aspects such as evidence synthesis, real-world data analysis, health economic modeling, and health technology assessment (HTA). Generative AI can serve as a valuable auxiliary tool, facilitating economic evaluations and potentially streamlining HTA submissions. However, these advancements come with inherent challenges and limitations that need careful consideration. As generative AI technologies advance, it is crucial for the HEOR community to continuously evaluate and adapt to these changes, ensuring that their integration into our workflows is both effective and ethically sound. This panel, convened by the ISPOR AI Working Group leaders, aims to explore current and promising approaches to integrating generative AI within HEOR.

OVERVIEW: The session, moderated by Jag Chhatwal, will begin with an exploration of the current generative AI landscape and its intersections with HEOR. Rachael Fleurence will illustrate practical applications, as well as limitations, of generative AI in systematic literature reviews, evidence synthesis and real-world evidence. Xiaoyan Wang will discuss the potential of generative AI in developing synthetic or external control arms, which could significantly expedite the evidence generation process for clinical trials. Jag Chhatwal will present the current uses of generative AI in health economic modeling, highlighting both the current capabilities and challenges. Finally, Dalia Dawoud will discuss how HTA may adapt evaluation methods and processes in response to submissions that employ generative AI.

The session will feature interactive polling to engage attendees actively and gather their insights. By bringing together diverse expert views, this session aims to foster a comprehensive understanding of the multifaceted roles, ongoing challenges, and future prospects of generative AI in HEOR. It will also address the existing uncertainties and skepticism surrounding the adoption of generative AI in HEOR.