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This living scoping review aims to map AI applications in implementation science, identify evaluation approaches, and synthesize research gaps.

February 14, 2026Open Access

Tracking the Evolving Role of Artificial Intelligence in Implementation Science: Protocol for a Living Scoping Review of Applications, Evaluation Approaches and Outcomes

Key Points

This living scoping review aims to map AI applications in implementation science, identify evaluation approaches, and synthesize research gaps.
Follows Joanna Briggs Institute methodology and Cochrane guidance for living systematic reviews.
Includes empirical studies, systematic reviews, grey literature, and policy documents related to AI applications.
Applies a search strategy across multiple bibliographic databases and relevant literature sources.
Two reviewers will screen studies and extract data related to AI characteristics and implementation tasks.
Data will be descriptively analyzed and synthesized narratively using the Knowledge-to-Action Model.
Consolidates evidence on AI applications in implementation science.
Identifies evaluation approaches and reported outcomes related to AI usage.
Highlights potential risks and methodological advancements for improved AI integration in practice.

Abstract

Background Artificial intelligence (AI) offers significant opportunities to improve the field of implementation science by supporting key activities such as evidence synthesis, contextual analysis, and decision-making to promote the adoption and sustainability of evidence-based practices. This living scoping review aims to: (1) map applications of AI in implementation research and practice; (2) identify evaluation approaches, reported outcomes, and potential risks; and (3) synthesize reported research gaps and opportunities for advancing the use of AI in implementation science. Methods This scoping review will follow the Joanna Briggs Institute (JBI) methodology and the Cochrane guidance for living systematic reviews. A living scoping review is warranted to keep up with the rapid changes in AI and its growing use in implementation science. We will include empirical studies, systematic reviews, grey literature, and policy documents that describe or evaluate applications of AI to support implementation science across the steps of the Knowledge-to-Action (KTA) Model. AI methods and models of interest include machine learning, deep learning, natural language processing, large language models, and related technologies and approaches. A search strategy will be applied to bibliographic databases (MEDLINE, Embase, CINAHL, PsycINFO, IEEE Xplore, Web of Science), relevant journals, conference proceedings, and preprint servers. Two reviewers will independently screen studies and extract data on AI characteristics, specific implementation task according to the KTA Model, evaluation methods, outcome domains, risks, and research gaps. Extracted data will be analyzed descriptively and synthesized narratively using a mapping approach aligned with the KTA Model. Discussion This living review will consolidate the evidence base on how AI is applied across the spectrum of implementation science. It will inform researchers, policymakers, and practitioners seeking to harness AI to improve the adoption, scale-up, and sustainability of evidence-based interventions, while identifying areas for methodological advancement and risk mitigation. Review registration Open Science Framework, May 2025: https://doi.org/10.17605/OSF.IO/2Q5DV

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Cite This Study

Fontaine et al. (Thu,) studied this question.

synapsesocial.com/papers/699010ce2ccff479cfe57138 https://doi.org/https://doi.org/10.12688/f1000research.171774.2

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