Generative AI for Health Technology Assessment: Opportunities, Challenges, and Policy Considerations

TL;DR

This paper reviews how generative AI and foundation models can transform health technology assessment by improving evidence synthesis, evidence generation, clinical trials, and economic modeling, while highlighting challenges and policy considerations.

Contribution

It provides a comprehensive overview of the applications, benefits, limitations, and policy implications of generative AI in health technology assessment, emphasizing responsible use.

Findings

Generative AI can automate literature reviews and meta-analyses with high accuracy.

These models can enhance real-world evidence generation from unstructured clinical data.

AI can optimize clinical trial design and streamline health economic modeling.

Abstract

This review introduces the transformative potential of generative Artificial Intelligence (AI) and foundation models, including large language models (LLMs), for health technology assessment (HTA). We explore their applications in four critical areas, evidence synthesis, evidence generation, clinical trials and economic modeling: (1) Evidence synthesis: Generative AI has the potential to assist in automating literature reviews and meta-analyses by proposing search terms, screening abstracts, and extracting data with notable accuracy; (2) Evidence generation: These models can potentially facilitate automating the process and analyze the increasingly available large collections of real-world data (RWD), including unstructured clinical notes and imaging, enhancing the speed and quality of real-world evidence (RWE) generation; (3) Clinical trials: Generative AI can be used to optimize trial design, improve patient matching, and manage trial data more efficiently; and (4) Economic modeling: Generative AI can also aid in the development of health economic models, from conceptualization to validation, thus streamlining the overall HTA process. Despite their promise, these technologies, while rapidly improving, are still nascent and continued careful evaluation in their applications to HTA is required. To ensure their responsible use and implementation, both developers and users of research incorporating these tools, should familiarize themselves with their current limitations, including the issues related to scientific validity, risk of bias, and consider equity and ethical implications. We also surveyed the current policy landscape and provide suggestions for HTA agencies on responsibly integrating generative AI into their workflows, emphasizing the importance of human oversight and the fast-evolving nature of these tools.