  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.
