Health Technology Assessment (HTA) is a cornerstone of evidence for informing health policy and resource allocation globally. Rapid advancements and the proliferation of digital health technologies and artificial intelligence (AI) have prompted the re-examination of HTA processes and methods. While traditional approaches are manual and labor-intensive, HTA processes are now exploring the use of AI and other digital technologies for automation, decision support, and evidence synthesis. To date, however, there have been very limited studies that map the innovative technological solutions of HTA, the models of integration, and the associated barriers, facilitators, and governance considerations. As such, this scoping review aims to address this critical gap by mapping the landscape of the global knowledge and practices related to AI and DTs used in and for HTA and identifying the key barriers and enablers influencing their adoption, integration, and effective application within HTA processes. A scoping review will be conducted between August and November 2025, following the Arksey and O’Malley framework, enhanced by Joanna Briggs Institute (JBI) recommendations, and reported according to Preferred Reporting Items for Systematic Reviews and Meta‑Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. Literature searches will be performed in electronic databases such as Medline (Ovid), Embase (Ovid), Global Health (Ovid), CINAHL (Ebsco), Scopus, Web of Science, and all regional indexes in the World Health Organization’s Global Index Medicus, and other region-specific sources for studies published between 2020 and 2025. Eligible studies will include peer-reviewed articles and grey literature describing the integration of digitization, automation, and AI in global HTA processes. Dual independent screening, data extraction, and quality appraisal will be employed. Findings from this review will provide a map of how digitization, automation, and AI are integrated into HTA practice, highlighting key enablers, barriers, and knowledge gaps. The insights will be used to better guide researchers, policymakers, HTA agencies, and AI developers, further supporting future research and implementation strategies for better informed decision-making.
Alkhaldi et al. (Thu,) studied this question.