A new report published in the Value in Health journal explores the use of generative artificial intelligence (AI) in health technology assessment (HTA). Written by Jagpreet Chhatwal, PhD, the report highlights how generative AI can transform HTA evidence generation methods but also stresses the importance of human oversight, transparency, and adherence to ethical standards.
The report identifies three key areas where generative AI, such as large language models (LLMs), can significantly impact HTA processes: systematic literature reviews, real-world evidence analysis, and health economic modeling. While generative AI shows promise in these areas, there are limitations such as issues of validity, reliability, bias, equity, and regulatory considerations that need to be carefully evaluated.
Chhatwal and coauthors provide suggestions for integrating generative AI into HTA workflows responsibly, including establishing clear guidance on LLM use, standardizing processes, focusing on health equity, and investing in training for the workforce. The authors are also developing a framework for evaluating the quality and rigor of LLM-assisted research.
ISPOR, the Professional Society for Health Economics and Outcomes Research, is leading the way in advancing HEOR excellence to improve global health decision-making. Value in Health, the society’s official journal, publishes original research and health policy articles to help healthcare leaders make evidence-based decisions. With an impact factor score of 4.9, Value in Health is a leading publication in the field of health economics and outcomes research.
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