Navigating the AI Revolution in Health Technology Assessments: Balancing Bias, Ethics, Time, Quality, and Evergreen Evidence
Author(s)
Moderator: Jackie Vanderpuye-Orgle, PhD, Access Consulting, Parexel International, Los Angeles, CA, USA
Speakers: Rito Bergemann, MD, PhD, Access Consulting, Parexel International (CH) AG, Loerrach, Germany; Sugandh Sharma, MSc, Parexel International, Mohali, Punjab, India; Dalia Dawoud, PhD, National Institute for Health and Care Excellence, London, LON, UK; Raquel Aguiar-Ibáñez, MSc, Merck Canada Inc, Kirkland, QC, Canada; Denise Meade, MBA, Microsoft, Norwell, MA, USA
Presentation Documents
Throughout this educational symposium, attendees will have the opportunity to delve deep into the responsible deployment of AI, recognizing the importance of reducing bias and upholding ethical standards. The session will also shed light on the optimization of processes and implementation, and the maintenance of high-quality standards in decision-making.
We will explore the potential applications of AI, while acknowledging the crucial role of evergreen or living evidence in the evaluation of healthcare interventions. Evergreen evidence entails the continuous integration of new data, allowing for dynamic decision-making that reflects real-world healthcare outcomes over time – but can pose a challenge for traditional static evidence approaches.
Learn how to navigate the complexities of incorporating evergreen evidence into the AI-driven HTA landscape, ensuring that decision-making frameworks remain adaptable, relevant, and informed.
The goal of this multi-stakeholder panel is to parse out the noise and begin to drive a consensus around defining the boundaries for utilizing AI in healthcare decision-making. The panel will focus on the responsible deployment of AI, with emphasis on reducing bias, maintaining high ethical standards, and balancing the time needed for preparation while maintaining quality.
Sponsored by Parexel International
Conference/Value in Health Info
Code
137
Topic
Methodological & Statistical Research