Related Experiment Video
Updated: Jan 8, 2026
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
User experience and adoption of automation and AI for evidence synthesis: a scoping review protocol
Chelsea Valenzuela1, Cindy Stern1,2, Edoardo Aromataris1
1JBI, School of Public Health, Faculty of Health and Medical Sciences, The University of Adelaide, SA, Australia.
Objective:
The objective of this scoping review will be to chart the available evidence on user experience and adoption of automation and artificial intelligence (AI) technologies for evidence synthesis.
Introduction:
Evidence syntheses are crucial for informing health care practice and policy; however, they are constrained by the ever-increasing volume of research and labor-intensive methods. With reviews often taking over a year to complete, automation and AI offer promising solutions by streamlining evidence synthesis workflows. However, while these technologies may offer significant time savings, their adoption depends on usability, trustworthiness, and workflow integration-elements that are currently poorly understood.
Eligibility Criteria:
This review will include primary research articles, all types of reviews, expert opinions, and gray literature that discuss user experience/adoption of automation and AI technologies for evidence synthesis across all disciplines.
Methods:
Following JBI scoping review methodology, the search strategy will identify published and unpublished evidence sources using a 3-step process. An initial exploratory search of PubMed was conducted to identify relevant keywords and terms. This will be followed by searches of PubMed, Web of Science Core Collection, Scopus, ProQuest Central, and ACM Digital Library databases, as well as online gray literature sources to identify eligible studies. A date limit of October 2015 will be applied to the searches, with no language limitations. Three reviewers will independently screen, select, and extract data from relevant evidence sources. Data extraction and analysis will be charted and mapped through the lenses of 4 distinct frameworks: Unified Theory of Acceptance and Use of Technology (UTAUT), Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM), Human-AI Interaction (HAI), and user experience (UX) principles.
Review Registration:
OSF https://osf.io/ayqjc/overview.
