ArticleClinical and public health guidelines2026
Integrating Artificial Intelligence and Automation Into Living Guideline Development: A Scoping Review of Existing Frameworks, Methods, and Applications.
Article in Clinical and public health guidelines, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI) and automation offer opportunities to enhance the efficiency, timeliness, and sustainability of living guidelines (LGs). However, how AI and automation have been integrated into existing LG development frameworks remains unclear. This scoping review represents the first step in a broader programme of work aimed at developing a framework which aims to guide responsible and coordinated AI adoption across all phases of LG development. Objective: To identify existing frameworks, methods, and approaches that integrate AI or automation into any stage of LG development. Methods: We conducted a structured search of PubMed, Embase, Web of Science, Scopus, Cochrane Database for Systematic Review and Cochrane Central Register of Controlled Trials from inception to March 12, 2026. We included peer-reviewed articles describing frameworks, models, or methods that applied AI or automation in the development of LGs. We extracted data into a standardised form, capturing study characteristics, AI methods, targeted guideline processes, and key findings and synthesised findings using a thematic narrative approach. Results: Of 1090 records identified, three studies met the inclusion criteria. The included studies described AI or automation applied to select components of the LG process, to support continuous evidence surveillance, to semi-automate study screening, and incremental updating of living systematic reviews. None addressed multiple stages of LG development in an integrated manner. Conclusions: Current evidence demonstrates fragmented and narrowly focused AI or automation applications within LG development processes. Our findings highlight opportunities for future work to develop and evaluate more comprehensive frameworks that span multiple stages of the LG lifecycle.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.