Systematic literature reviews (SLRs) are critical for health technology assessment (HTA) and regulatory submissions. However, the volume of published studies and the complexity of data extraction make SLRs labour-intensive and time-consuming. Artificial intelligence (AI), in particular natural language processing (NLP) and large language models (LLMs), has been progressively integrated into SLR workflows to support evidence identification and structured data extraction, especially for the core elements of Population, Intervention, Comparator, Outcomes, and Study design (PICOS). 

Generative artificial intelligence (AI) is swiftly transforming industries, and health technology assessment (HTA) is no exception. A recent ISPOR Working Group Report, published in Value in Health, examines how generative AI, including large language models (LLMs), can improve HTA processes while emphasising the necessity for human oversight.