Clinical and public health guidelines · 2026
Integrating Artificial Intelligence and Automation Into Living Guideline Development: A Scoping Review of Existing Frameworks, Methods, and Applications.
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Ismaila Nofisat, Naqvi Syed Arsalan Ahmed, Mbuagbaw Lawrence, Ma Jinhui, Thabane Lehana
Аннотация
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.
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.