Newswise — Lawrenceville, NJ, USA—February 11, 2025—Today, Value in Health, the journal of ISPOR—The Professional Society for Health Economics and Outcomes Research, shared a new ISPOR Working Group Report about generative artificial intelligence (AI) in health technology assessment (HTA). The report, titled “Generative AI for Health Technology Assessment: Opportunities, Challenges, and Policy Considerations—An ISPOR Working Group Report,” is part of the February 2025 issue of Value in Health.
According to Jagpreet Chhatwal, PhD, from Massachusetts General Hospital, generative AI could change how HTA evidence is created. He stresses that it is vital to maintain human oversight and ethical practices as we explore these new possibilities. Chhatwal also works as an Associate Professor at Harvard Medical School.
The report highlights three main areas where generative AI, particularly large language models (LLMs), can impact HTA:
- Systematic Literature Reviews: AI can help streamline the process by suggesting search terms, screening abstracts, and even creating code for meta-analyses.
- Real World Evidence: Generative AI can analyze large sets of real-world data, including clinical notes and imaging, making it easier to derive insights.
- Health Economic Modeling: AI can support different stages of model development, potentially boosting efficiency in HTA.
Though promising, generative AI is still in early stages and faces challenges, such as ensuring scientific accuracy and avoiding biases. These factors need careful review and human supervision moving forward.
The authors suggest HTA agencies adopt clear guidelines for using generative AI. They recommend standardizing processes and focusing on health equity to improve analyses. Training programs for HTA staff will also be crucial for effective AI use.
Chhatwal points out that generative AI offers tools that could enhance evidence generation for HTA. However, the complexity of these models means that understanding and evaluating their results will remain a challenge. Over time, both the use of these models and their performance are expected to improve.
Coauthors of the report include experts like Rachael L. Fleurence and Jiang Bian, among others. They are working on a framework to help professionals in health economics and outcomes research evaluate the effectiveness of AI-assisted studies. For more on ISPOR’s AI initiatives, visit here.
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ABOUT VALUE IN HEALTH
Value in Health is a well-regarded journal that publishes research and health policy articles to aid in health economics decision-making. It circulates to over 55,000 readers and holds a strong impact factor score.
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