Northwestern researchers help chart the future of AI in implementation science

September 4, 2026

Artificial intelligence is transforming how health care, public health, and implementation science operate — and Northwestern University’s Department of Medical Social Sciences (MSS) and the Center for Dissemination and Implementation Science (CDIS) hope to be at the forefront of understanding what that transformation will mean for the field. A new publication led by Guillaume Fontaine, PhD, at McGill University, and co-authored by Northwestern researchers, "Artificial intelligence in implementation science and practice: a living scoping review," offers the first comprehensive, field-wide synthesis of how AI is being used or proposed for use in implementation science and practice.

"Implementation science has always been about closing the gap between what we know works and what happens in practice. AI gives us powerful new tools to help close that gap, but only if we approach it with the same rigor that we'd expect of any other tool we use in our work. That's exactly what this review sets out to do," said co-author and chair of the Department of Medical Social Sciences, Rinad Beidas, PhD.

A rapidly growing field

The review searched six databases through April 6, 2026, identifying 7,203 records and ultimately including 40 sources — 85% of which were published since 2021, underscoring just how quickly this area of research is accelerating. That pace is part of why this paper matters: as the use of AI in implementation research and practice evolves rapidly, the authors encourage the field to critically evaluate when and how AI use is effective to help build the evidence base and inform future integration of AI into implementation work.

The review found AI is already being used or proposed across multiple stages of implementation — most often in evaluation (25 of 40 sources), strategy selection and tailoring (23 sources), assessing barriers and facilitators (21 sources), and implementation monitoring (20 sources).

On the other hand, areas like adaptation, prioritization, sustainment, and scale-up remain underdeveloped, and the evidence base overall was described as "development-heavy but evaluation-light" — strong on building new tools, thinner on rigorously testing them for fairness, robustness, cost, and equity.

Building the evidence responsibly

Among the 31 sources examining a specific AI system, conventional machine learning was most common, followed by generative AI and multicomponent systems. Implementation outcomes were reported in 25 of those sources; technical performance in 19. Human-centered outcomes, equity, cost, and efficiency were assessed far less often — a gap Northwestern researchers see as central to the next phase of this work.

Sara Becker, PhD, Director of the CDIS, emphasized, "This scoping review highlights the nascency of AI tools in implementation research and practice. There is a clear need for rigorous research that attends to who AI tools serve, who they leave out, and under what conditions they improve implementation methods, processes, and outcomes. That's precisely the type of work our center hopes to lead.”

Looking ahead

This review is the first cycle of a living scoping review — meaning the review will be updated based on new evidence that has emerged every six to 12 months — and it represents the first step in a larger effort called AI Methods for Implementation Science (AIM-IS). AIM-IS will use the review’s findings, together with stakeholder consultation, consensus methods, and usability testing, to build a framework, toolkit, and reporting standard for responsible AI use in implementation research and practice.

We're also excited to share that the project lead, Guillaume Fontaine, PhD, will be speaking as part of the Network for Collaborative Intelligence (NCI) Distinguished Speaker Series — a new Northwestern University-wide AI initiative — on November 3, 2026. Fontaine's scholarship on AI and implementation science methods reflects the kind of forward-looking, rigorous work our department is proud to support. Given how fertile the environment at Northwestern is right now for both AI and implementation science, we expect his talk will help germinate new lines of research for our faculty and open the door to potential collaborations across schools and centers.

MSS and CDIS are proud to be a part of helping shape this nascent field’s trajectory. As AI continues to influence how implementation science is done, Northwestern is helping ensure the field grows in a way that's rigorous, equity-attentive, and grounded in real-world impact. Congratulations to the authors on this impactful contribution.

Authors: Guillaume Fontaine, PhD; Olivia Di Lalla; Rachael Laritz; Jeremiah Durran; Chi Zhang; Jeffery Chan; Laura Crump; Alenda Dwiadila Matra Putra; Samira Abbasgholizadeh-Rahimi; Ruopeng An; Rinad S. Beidas, PhD; Christine Fahim; Elvin Geng; Ian D. Graham; Janna Hastings; Sylvie D. Lambert; France Légaré; Susan Michie; Byron J. Powell; Justin Presseau; Joseph Elias; Thomas Rudge; Sharon E. Straus; James Thomas; Vivian Welch; and Natalie Taylor.