Plain Language Summary

What is it about? Generative artificial intelligence (AI), a branch of AI that creates new content, is increasingly influencing health economics and outcomes research. This technology is important because it can significantly improve efficiency and accuracy in various research tasks. However, the challenge lies in ensuring the scientific reliability of AI-generated outputs. The manuscript identifies gaps in the current understanding of AI's application in this field and suggests methods to improve its accuracy and dependability. The article contributes to a better understanding of how generative AI can transform health economics research by highlighting opportunities and challenges.

How was the research conducted? The article is based on a comprehensive review and synthesis of generative AI applications in health economics. Researchers identified uses of GenAI to explore systematic literature reviews, health economic modeling, real-world evidence generation, and dossier development. They reviewed these applications to understand the potential and limitations of AI in each area.

What were the results? The article found that generative AI holds significant potential in health economics and outcomes research, particularly in enhancing efficiency and productivity. Additional findings include AI's ability to streamline complex processes, such as systematic reviews and economic modeling. However, the article notes the persistent challenge of ensuring the scientific accuracy of AI-generated outputs, which remains a barrier to its full integration into research practices.

Why are the results important? These results are important for health technology assessment agencies as the findings highlight the need for guidelines and standards when using AI tools. In practical terms, these findings suggest that AI has the potential to make research processes faster and more efficient. Healthcare professionals, researchers, and decision makers can all benefit from these advancements by improving the quality and speed of research. Long-term, the results could lead to more innovative healthcare solutions and improved patient outcomes.

What are the strengths and weaknesses of this study? The article's strength lies in its comprehensive overview of AI applications across multiple domains within health economics. However, a limitation is that the field is advancing rapidly and the article may not include the latest applications.

By providing a clear understanding of generative AI's potential and challenges in health economics, the review offers valuable insights for patients, healthcare decision makers, and researchers. It emphasizes the need for ongoing research and collaboration to address current limitations and harness AI's full potential in improving healthcare outcomes.

^{Note: This content was created with assistance from artificial intelligence (AI) and has been reviewed and edited by ISPOR staff. For more information or for inquiries on ISPOR’s AI policy, click here or contact us at info@ispor.org.}