A Taxonomy of Generative Artificial Intelligence in Health Economics and Outcomes Research: An ISPOR Working Group Report

Objectives This article presents a taxonomy of generative artificial intelligence (AI) for health economics and outcomes research (HEOR), explores emerging applications, outlines methods to improve the accuracy and reliability of AI-generated outputs, and describes current limitations. Methods Foundational generative AI concepts are defined, and current HEOR applications are highlighted, including for systematic literature reviews, health economic modeling, real-world evidence generation, and dossier development. Techniques such as prompt engineering (eg, zero-shot, few-shot, chain-of-thought, and persona pattern prompting), retrieval-augmented generation, model fine-tuning, domain-specific models, and the use of agents are introduced to enhance AI performance. Limitations associated with the use of generative AI foundation models are described. Results Generative AI demonstrates significant potential in HEOR, offering enhanced efficiency, productivity, and innovative solutions to complex challenges. Although foundation models show promise in automating complex tasks, challenges persist in scientific accuracy and reproducibility, bias and fairness, and operational deployment. Strategies to address these issues and improve AI accuracy are discussed. Conclusions Generative AI has the potential to transform HEOR by improving efficiency and accuracy across diverse applications. However, realizing this potential requires building HEOR expertise and addressing the limitations of current AI technologies. Ongoing research and innovation will be key to shaping AI’s future role in our field.