Living evidence and gap maps: a scoping review of automation and living mode parameters reported in 44 digital...

To examine how continually updated, living evidence and gap maps (L-EGMs) with an online presence report planned update schedules, retirement plans, living status, use of automation across review stages, and the methodological guidance cited to support their conduct and reporting.

A cross-sectoral scoping review of digital L-EGM interfaces, which act as foundational support tools for decision-makers by providing a visual and interactive summary of all available evidence, as well as evidence gaps. Targeted searches were conducted in Google search engine (June 2022 and April/September 2025), Web of Science Core Collection and MEDLINE (January 2026), supplemented by records from a methodology review and additional EGMs found through supporting documentation of included maps, or known to the research team.

Forty-four L-EGMs, predominantly health sector-related, met the eligibility criteria. Half of the digital interfaces cited big picture review guidance in their associated documentation, 11% cited (living) systematic review guidance, and 39% did not cite any overarching synthesis typology or living evidence synthesis guidance. Fifty-seven percent reported a fixed update schedule with planned update frequencies varying from daily to every 2 years (median: 1 month), but most did not clarify whether schedules differed across update stages. Only 14% reported retirement plans and 39% indicated whether the L-EGM is still living. Automation or semiautomation was reported in 70% of L-EGMs. Fifty-nine percent used it for searching, 45% for screening and 34% for coding, with many reporting automation across multiple stages. In addition, three L-EGMs used a natural language processing-based risk of bias assessment tool. Twenty-five percent of L-EGMs reported context-specific automation performance metrics or validation approaches.

We identified a growing body of digital, living EGMs that use automation-especially machine learning-that could be systematically reviewed. Reviewers and methodologists should further assess the potential for automating EGMs, their actual living mode parameters, methodological changes, and how these are reported across web-based versus conventional outputs, working toward a consensus on map-specific guidance. Until such guidance is established, authors of living EGMs can follow existing recommendations for responsible automation, living systematic reviews, and other living evidence syntheses PLAIN LANGUAGE SUMMARY: This study examined 'living' evidence and gap maps. These maps are online tools that show where research evidence exists and where important gaps remain. They are meant to be updated regularly as new research is published. The authors identified 44 living evidence and gap maps (L-EGMs) and looked at how they are updated, whether they use automated methods, and how clearly these methods are described. They found that L-EGMs are becoming more common, especially in health research, and are often built using specialised software platforms. Most maps claimed they are updated at regular intervals, but few clearly reported whether they are still being updated, how often key steps (like incorporating new studies) are repeated, or whether there is a plan to stop updating them in the future. About 70% of the maps used some form of automated method, most often to find new studies and help decide whether they were relevant to include. Over half used machine learning (ML), of which a small number used newer artificial intelligence methods. However, authors rarely explained how these tools were used or tested, and there was very little evidence on whether these automated methods were efficient and accurate. The study also found that many L-EGMs do not follow existing guidance. The authors conclude that tailored guidance is urgently needed to ensure that L-EGMs are transparent and reliable. This is important if they are used to help allocate limited research resources, for example, in health care.