Harnessing data science and artificial intelligence to advance implementation research and practice

Implementation science aims to bridge the gap between research evidence and routine healthcare practice by understanding and optimizing the integration of evidence-based interventions (EBIs). In this paper, we identify seven persistent challenges limiting implementation progress, including (1) overwhelming volume of implementation materials (e.g., reports, interviews, surveys), (2) contextual variability, (3) complex interactions among contextual factors, interventions, and outcomes, (4) stakeholder engagement constraints, (5) equity and access barriers, (6) insufficient or biased data, and (7) concerns around data security, trust, and ethics. We explore how advances in data science and artificial intelligence (AI) offer promising solutions to these challenges by enhancing evidence extraction and synthesis, contextual analysis, stakeholder engagement, and adaptation throughout the implementation process. Using a live project in precision oncology as a practical example, we demonstrate how AI tools, such as large language models (LLMs), clustering algorithms, and sentiment analysis, can support implementation research and practice, from concept (e.g., barriers, facilitators, strategies) extraction and barrier identification to process mapping. We also introduce ImpleMATE, an AI-enabled platform that integrates implementation science knowledge with dynamic learning health system workflows, enabling continuous knowledge extraction, decision support, and feedback for implementation improvement. While AI offers significant potential to accelerate and scale implementation efforts, we emphasize the need for ethical oversight, transparency, and human collaboration to ensure responsible, equitable, and impactful application in practice.