The healthcare landscape is undergoing a profound transformation with the integration of artificial intelligence (AI) meaning that health technology assessment (HTA) stands at a critical crossroads. While a previous discussion examined how generative AI is reshaping traditional HTA processes, a more sophisticated and promising development is emerging: agentic deep research. This approach represents a fundamental reimagining of how evidence is generated, synthesised, and applied to inform healthcare decision-making.

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.