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Artificial intelligence in implementation research: a scoping review of applications and recommendations
Jiani Ma1, Hanlu Shi2, Yuxin Zhang1
1Institute for Physical Activity and Nutrition (IPAN), School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC, Australia.
Background:
Artificial 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.
Aims:
This 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.
Methods:
This 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.
Results:
Included 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.
Conclusions:
The 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.