Automating Evidence Synthesis in Implementation Science: A Framework for Navigating Benefits and Challenges

Background: Implementation science consistently struggles to convert research evidence into practice because resource-heavy synthesis methods lag behind the growing body of literature. Conventional systematic reviews take 6 to 24 months to finalize, resulting in a gap between when evidence becomes available and when implementation decisions need to be made. Although recent developments in artificial intelligence and large language models present potential remedies, they also bring up worries regarding preserving implementation science's essential principles, including contextual sensitivity, stakeholder involvement, and equity. Methods: We developed a thorough integration framework by systematically analyzing empirical evidence on automated synthesis performance, assessing requirements specific to implementation science, and utilizing the Exploration, Preparation, Implementation, and Sustainment framework to formulate practical recommendations. Our analysis explored the capabilities and limitations of automated synthesis across nine dimensions essential to implementation science practices. Results: Empirical research indicates that automated synthesis can reduce the time needed for screening and data extraction tasks by 50-95%, all while achieving accuracy levels similar to those of human reviewers. These automated systems facilitate ongoing evidence monitoring and the execution of living systematic reviews that were once deemed unrealistic due to limited resources. Nevertheless, there are notable shortcomings in capturing contextual nuances; for instance, large language models only reach 13.8% accuracy in reference retrieval tasks and face consistent difficulties in interpreting qualitative implementation research. Our framework offers phase-specific guidance to ensure responsible integration, prioritizing human-AI collaboration instead of replacement and incorporating systematic equity safeguards during implementation processes. Discussion: Automated evidence synthesis has the potential to significantly bridge the evidence-to-practice gap in implementation science, though it must be carefully aligned with core field values. Achieving success requires a deliberate approach that harnesses efficiency improvements while upholding the importance of human insight in contextual understanding and stakeholder involvement. The framework offers organizations a structured strategy for adopting automated synthesis; however, empirical validation through pilot projects and studies comparing effectiveness is crucial to assess real-world effects and improve integration strategies.