ArticleTranslational behavioral medicine2026
Artificial intelligence in implementation research: a scoping review of applications and recommendations.
Article in Translational behavioral medicine, 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundArtificial intelligence (AI) holds considerable potential for addressing challenges in knowledge translation and implementation research, including increasing the speed of knowledge synthesis, tailoring of implementation strategies, and collection of implementation data. To date, no prior review has systematically examined how AI has been applied within implementation research.
aimsThis scoping review aimed to synthesize the current evidence on applying AI in implementation research, identify reported outcomes and challenges, including ethical, regulatory, and practical considerations.
methodsThis review is reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping review checklist. A systematic search was conducted across MEDLINE Complete, Scopus, IEEE Xplore, and ACM Digital Library on 31 July 2025. Seven peer-reviewed empirical studies and methodology papers were included and narratively synthesized.
resultsIncluded studies applied machine learning, natural language processing, and generative AI to support implementation monitoring, strategy selection, literature synthesis, and knowledge dissemination. These applications may help challenges related to speed, analytic burden, sustainability, and contextual tailoring in implementation research. Ethical considerations included privacy, transparency, and equity.
conclusionsThe current evidence base reflects an early stage of AI application in implementation research. While offering important insights, it highlights opportunities for deeper theoretical integration and more robust empirical evaluation to advance translation of research into practice.
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What OpenQuestion holds
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.