Abstract
Evidence synthesis (ES) involves rigorous, reproducible methodologies, which are increasingly being presented as 'Living' systematic reviews. As such, ES are critical to evidence-informed decision-making processes, such as the development, implementation, evaluation and monitoring of health technology assessments, practice guidelines and policies. However, the ES process is time-intensive, typically requiring months or years and extensive manual effort. Technological advancements, particularly artificial intelligence (AI), offer opportunities to automate various ES steps, potentially increasing efficiency and reducing costs. AI tools and platforms, including large language models (LLMs), facilitate faster ES through advanced natural language processing (NLP) capabilities. Despite their potential, AI tools have limitations, including risks of automation bias and lack of true semantic understanding, requiring careful evaluation to ensure trustworthiness. We conducted the first scoping review to update and map all data science tools, including LLMs, which are either being developed and/or deployed to optimise ES steps and assess their impact in both low- and middle-income countries (LMICs) and high-income countries (HICs). Our scoping review identified 137 studies and 388 of such AI tools and platforms to respond to the World Health Organization's call for safe and ethical AI in health, documenting the current landscape to identify barriers and facilitators to equitable and sustainable access for glocal researchers. We further outline three recommendations: (1) promote collaborative AI platforms ensuring equity of access to include gap regions identified (Latin America, Africa, Middle East), (2) establish evaluation standards for methods testing and reporting, and (3) emphasise human input and multidisciplinary capacity building for developing and implementing AI tools in ES.