Artificial intelligence in implementation science and practice: a living scoping review

Artificial intelligence (AI) encompasses computational systems that perform tasks typically requiring human intelligence, including machine learning, generative AI, and agentic applications. The use of AI to support implementation activities is growing, but its applications and evaluation remain poorly characterised. We conducted the first cycle of a living scoping review to identify how AI is being used and evaluated across implementation science and practice. We followed JBI and Cochrane guidance and reported findings per PRISMA-ScR and PRISMA-LSR. We searched six databases through April 6, 2026, and included sources describing or evaluating AI to support implementation research or practice activities. We excluded adjacent uses such as knowledge synthesis automation. We extracted study characteristics, AI approaches, implementation tasks, evaluation methods, outcomes, and risks, and synthesised findings descriptively. We identified 7,203 records and included 40 sources, 34 (85%) of which were published since 2021. Thirty-one sources described, developed, or evaluated a specific AI system, comprising 22 primary research articles, three protocols, three conference abstracts, and three other sources. The remaining nine conceptual, methodological, framework or review articles discussed potential applications of AI in implementation science. Across all 40 sources, AI was most often used or proposed to support evaluation (25/40), implementation strategy selection and tailoring (23/40), barrier and facilitator assessment (21/40), and implementation monitoring (20/40). Among the 31 sources involving a specific AI system, the use of conventional machine learning was most common (10/31), followed by generative AI (7/31), multicomponent AI systems or studies comparing AI approaches (5/31), and non-generative deep learning (3/31). Implementation-related outcomes were reported or prospectively specified in 25/31 sources, technical performance in 19/31, human-centred outcomes in 12/31, time or efficiency in 10/31, clinical or health system outcomes in 9/31, equity in 3/31, and cost or resource outcomes in 2/31. Risks were discussed in about half of sources, although assessment of harms and environmental or societal consequences was rare. AI is in a nascent stage of supporting implementation science and practice. Reported use is narrow and methodologically underdeveloped, and much routine use is likely unpublished. The field needs rigorous, comparative, prospective, and equity-attentive research to establish whether and how AI improves implementation methods, processes, and outcomes. Open Science Framework, May 2025: https://doi.org/10.17605/OSF.IO/2Q5DV