Session (CTI)

ISSUE: Artificial intelligence (AI) is moving from experimental use to routine application across HEOR, including evidence synthesis, economic modeling, real-world evidence, comparative effectiveness research, and value communication. While attention focus is heavily placed on software capabilities, a fundamental challenge has emerged: who is qualified to lead AI-enabled HEOR workflows? Should HEOR professionals acquire technical expertise to direct and govern AI analyses, or should AI data scientists be cross-trained in health economics, epidemiology, and clinical evidence evaluation? The answer impacts scientific quality, organizational structure, and the baseline identity of the HEOR profession. OVERVIEW: This panel explores competing approaches to building the next generation of AI-native HEOR capabilities. Ramiro Gilardino will frame the structural debate, focusing on the friction between advanced engineering agility and the deep methodological rigor required for healthcare decision-making. Jaime Caro will argue that robust HEOR necessitates expert mastery of causal inference, economic evaluation, and evidence synthesis, meaning AI tools must be adopted safely under the strict stewardship of grounded domain specialists. Manuel Cossio will counter that sophisticated multi-agent AI architectures and automated analytical workflows demand native data science expertise, making the training of technical specialists in core HEOR principles the only scalable path forward. Mitch Higashi will provide an institutional leadership perspective, detailing future-ready competency frameworks, dual-mentorship governance models, and collaborative workforce strategies that move past binary either-or approaches. Through moderated debate and real-time audience polling, this interactive session outlines non-delegable competencies and details how global organizations must prepare for an AI-integrated future.