June 25, 2026
Title: Beyond the Research Assistant: Innovative Uses of AI in Health Preference Research
Thursday, June 25, 2026
10:00AM EDT | 2:00PM UTC | 4:00PM CEST
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Description
Artificial intelligence (AI) is rapidly expanding the possibilities for Health Preference Research (HPR), moving beyond research assistance toward applications that transform how preference data are analyzed and interpreted. While assistive AI tools can support tasks such as text generation, survey assistance, and qualitative coding, transformative applications integrate AI into the core analytical stages of preference research, using it to synthesize evidence across studies, model complex decision-making processes, and predict patient treatment preferences. This webinar will explore why these applications are becoming increasingly relevant in health preference research today.
As part of ISPOR's Health Preference Research Special Interest Group's mission to advance the development, implementation, and use of health preference research in support of health policy, development of medical products, and patient care, this session aims to illustrate how AI-enabled approaches may reshape elements of the research lifecycle, while also highlighting validation challenges, oversight requirements, and practical constraints. Applied case examples will demonstrate how AI can support evidence synthesis and preference prediction, alongside a structured discussion of methodological implications, validation challenges, and governance considerations.
Michael Bui, MSc, will introduce the distinction between assistive and transformative AI applications, highlight why AI is becoming relevant in HPR, and discuss the types of AI tools in scope, including meta-analysis and preference prediction. Paola Berchialla, PhD, will present AI-enabled meta-analysis workflows, including automated attribute extraction, standardization across studies, and meta-analytic pooling, with methodological insights, limitations, and a live demonstration. Semra Ozdemir, PhD, will present AI-driven preference prediction, exploring how AI can anticipate patient treatment choices and its implications for research and decision-making.
By combining practical demonstrations with critical appraisal, participants will gain insight into emerging AI-enabled approaches in HPR that extend beyond currently published guidance.
Learning Objectives
- How transformative AI applications differ from assistive AI tools, and how AI can be embedded into core analytical workflows in health preference research (HPR).
- How emerging AI-enabled methodologies are being applied to evidence synthesis and preference modeling within HPR.
- What methodological, validation, and oversight considerations accompany the integration of AI into core research processes.
- Under what conditions AI-enabled approaches may add value, and where important limitations or risks remain.
Speakers:
Jaein Seo, PharmD, MHS, Senior Research Associate, Thermo Fisher Scientific, Wilmington, NC, USA
Michael Bui, MSc, PhD candidate, University of Twente, Enschede, The Netherlands
Paola Berchialla, PhD, Full Professor, University of Torino, Torino, Italy
Semra Ozdemir, PhD, Associate Professor, Duke University, Durham, NC, USA
Brought to you by ISPOR Health Preference Research Special Interest Group
Please note: On the day of the scheduled webinar, the first 1000 registered participants will be accepted into the webinar. For those who are unable to attend, or would like to review the webinar at a later date, the full-length webinar recording will be made available at the ISPOR Educational Webinar Series webpage approximately 2 days after the scheduled Webinar. Reservations are on a first-come, first-served basis.