Session (CTI)

PURPOSE: The objectives of this session are to introduce the NICE AI Methods Framework, to discuss what would make AI acceptable and trustworthy in HTA decision-making, and to gather stakeholder perspectives to inform public feedback. DESCRIPTION: AI has the potential to be transformative across HTA. However, there is limited consensus regarding acceptable use, evidence standards, transparency, reproducibility, and oversight for AI applications in HTA. This session will introduce the NICE AI Methods Framework, a best practice framework developed to support the use of AI throughout the HTA pipeline. The session will begin with an introduction outlining the importance of responsible AI in HTA, the immediate need for best practice guidance to enable innovation, and the global context for AI governance and implementation in evidence generation and decision-making (7 minutes, Higashi). A walkthrough of the AI Framework will then be presented, including the draft AI principles and best practice guidance for specific use cases; areas for stakeholder feedback will also be highlighted (10 minutes, Sonabend-Friend). A 7-minute reflection on the Framework will be provided by Klijn. The session will continue with a moderated discussion exploring what would make AI acceptable and trustworthy in HTA decision-making (24 minutes, Higashi moderating; Sonabend-Friend, Fotheringham, Klijn, and Ho participating). Discussion topics will include opportunities enabled through AI, priority use cases for the framework, and operational considerations for implementing best practice AI methods guidance. The session will conclude with audience interaction, including live polling, targeted discussion prompts, and selected audience questions (12 minutes, moderated by Higashi). Illustrative topics will include current AI usage, organizational readiness for AI in HTA, barriers to adoption, and challenges in operationalizing AI frameworks. This session may benefit HTA agencies, payers, industry, HEOR professionals, policymakers, and researchers involved in evidence generation and decision-making.