A new report released by ISPOR—The Professional Society for Health Economics and Outcomes Research—has spotlighted the transformative potential of generative artificial intelligence (AI) in reshaping the landscape of health technology assessment (HTA). Dr. Jagpreet Chhatwal, Director of the Institute for Technology Assessment at Massachusetts General Hospital and a leading voice in the application of AI in health economics and outcomes research (HEOR), contributed to the expert panel that shaped the report’s findings.
The ISPOR report, titled “Generative AI for Health Technology Assessment: Opportunities, Challenges, and Policy Considerations” underscores how generative AI is accelerating evidence generation, streamlining systematic reviews, and enabling economic modeling. These advancements are poised to make HTA processes more efficient, transparent, and responsive to the growing complexity of the HEOR field. Here is the official press release: https://www.ispor.org/heor-resources/news-top/news/2025/02/11/generative-ai-set-to-reshape-health-technology-assessment–ispor-report-finds
The report identifies 3 key areas where generative AI, including large language models (LLMs), could significantly impact HTA processes:
- Systematic Literature Reviews: Generative AI has the potential to assist in automating aspects of systematic literature reviews by proposing search terms, screening abstracts, extracting data, and generating code for meta-analyses.
- Real World Evidence: Generative AI demonstrates capability in analyzing large collections of real-world data, including unstructured clinical notes and imaging, making it easier to extract valuable insights from complex healthcare data.
- Health Economic Modeling: AI tools can assist in various stages of health economic model development, from conceptualization to validation, potentially increasing efficiency in this critical aspect of HTA.
“The field of generative AI provides potentially transformative tools to augment and support the efficient generation of evidence to support HTA under human supervision,” noted Chhatwal. “Because of the nature of these models, there is added complexity in using them and evaluating the validity and reproducibility of their results. It is expected, as with all new technologies, that both user expertise with foundation models, and the actual performance of foundation models themselves, will improve rapidly in the near future.”
With rapid advancements in AI capabilities, ISPOR’s report calls for proactive collaboration among researchers, regulators, and technology developers to ensure that generative AI is leveraged effectively and responsibly across the global HTA community.
The full report is available on the ISPOR website [link].