OBJECTIVES: To assess the uptake of Artificial Intelligence (AI) in Health Economics and Outcomes Research (HEOR) based on studies presented at the ISPOR USA 2024 conference.

METHODS: Systematic literature research methods were employed to identify studies, presented at the ISPOR USA 2024, incorporating AI. Search terms included “Generative AI”, “Artificial Intelligence”, “Machine Learning”, and “Large Language Models”. For the identified studies, posters were reviewed and qualitatively and quantitatively assessed.

RESULTS: The search identified 83 unique studies incorporating AI. Studies relevant to AI were grouped into four HEOR areas: 1) Systematic Literature Reviews and Meta-Analyses, 2) AI-Driven Economic Modeling and Cost-Effectiveness, 3) AI Applications in Data Extraction and Synthesis, and 4) Patient-Centric Outcomes and Engagement Using AI. Content analysis showed dominant interest in 'Patient-Centric Outcomes and Engagement Using AI' and 'AI Applications in Data Extraction and Synthesis,' with 25 (30%) and 22 (27%) studies respectively. Nineteen (23%) studies were 'Systematic Literature Reviews and Meta-Analyses in HEOR' whereas 'AI-Driven Economic Modeling and Cost-Effectiveness' was used in 17 (20%) studies. AI-relevant studies represented almost one quarter of the total studies presented in the Methodological & Statistical Research category at the ISPOR USA 2024 showing a consistent diachronic increase in the proportion of AI-relevant studies.

CONCLUSIONS: Findings show a clear trend towards integrating AI, with the aim to facilitate and accelerate HEOR evidence generation and analysis. The results also highlight the versatility and wide-ranging applicability of AI technologies within HEOR. Researchers in the field of HEOR, increasingly recognize the benefits of using AI which has the potential to transform healthcare decision-making and policy development.