Introduction to Applied Generative AI for HEOR

The rapid advancement in generative artificial intelligence (Gen AI) presents an opportunity for transformative potential in the field of Health Economics and Outcomes Research (HEOR). This course provides an introductory understanding of generative AI models with a particular focus on large language models (LLMs), which are revolutionizing the field of HEOR. Participants will be provided with an overview of the most appropriate ways to access LLMs, going beyond the use of chatbots. Further, they will be given insights into how to use prompt engineering to conduct scientific research and gain an understanding on issues pertaining to privacy and security when using Gen AI for HEOR. Participants will further explore specific applications of these models for conducting robust scientific HEOR research in systematic literature review (SLR), real-world evidence analysis, and economic evaluation. The course aims to equip participants with the knowledge to begin to use generative AI techniques for specific HEOR contexts and to appreciate how these innovative approaches can enhance HEOR activities. Practical exercises using Python and relevant AI frameworks will be incorporated for participants to follow along. Participants who wish to gain hands-on experience are required to bring their laptops with Python installed.

PREREQUISITES: Students should have a general understanding of common HEOR concepts such as SLRs and cost-effectiveness models. Knowledge of Python or similar programming languages such as R is considered a benefit, but not required.