Evidence map›Paper›PMID 41975498›Full record

ArticleImplementation science : IS2026

AI Methods for Implementation Science (AIM-IS): developing a framework, toolkit, and reporting standard for the responsible use of AI in implementation practice and research.

Guillaume Fontaine, Susan Michie, Rinad S Beidas, Elvin Geng, Christine Fahim, Byron J Powell, Vivian Welch, James Thomas, Jeffery Chan, Samira Abbasgholizadeh-Rahimi and 8 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Implementation science : IS, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Guillaume FontaineIngram School of Nursing, Faculty of Medicine and Health Sciences, McGill University, 680 Sherbrooke West, #1812, Montréal, QC, H3A 2M7, Canada. guil.fontaine@mcgill.ca.
Susan MichieCentre for Behaviour Change, University College London, London, UK.
Rinad S BeidasDepartment of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
Elvin GengDivision of Infectious Diseases, School of Medicine, Washington University in St. Louis, St. Louis, MO, USA.
Christine FahimLi Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, ON, Canada.
Byron J PowellBrown School, Washington University in St. Louis, St. Louis, MO, USA.
Vivian WelchSchool of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada.
James ThomasEPPI Centre, Social Research Institute, University College London, London, UK.
Jeffery ChanSchool of Population Health, UNSW Sydney, Sydney, NSW, Australia.
Samira Abbasgholizadeh-RahimiDepartment of Family Medicine, McGill University, Montreal, QC, Canada.
France LégaréDepartment of Family and Emergency Medicine, Université Laval, Québec City, QC, Canada.
Janna HastingsIdiap Research Institute, Martigny, Switzerland.
Sylvie D LambertIngram School of Nursing, Faculty of Medicine and Health Sciences, McGill University, 680 Sherbrooke West, #1812, Montréal, QC, H3A 2M7, Canada.
Justin PresseauMethodological and Implementation Research Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada.
Sharon E StrausLi Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, ON, Canada.
Ruopeng AnSilver School of Social Work, New York University, New York, NY, USA.
Ashrita SaranGlobal Development Network, New Delhi, India.
Natalie TaylorSchool of Population Health, UNSW Sydney, Sydney, NSW, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI), including machine learning, natural language processing, and large language models, may support implementation practice and research in tasks such as evidence synthesis, determinant assessment, strategy selection, monitoring, adaptation, and theory development. However, these applications of AI do not form a single, uniform category. They span a continuum from practice-facing applications that support local implementation work to research- and methods-facing applications that support evidence generation and synthesis. The guidance on how to classify, evaluate, and report these uses of AI remains limited. The AI Methods for Implementation Science (AIM-IS) program aims to develop, validate, and maintain a suite of products to guide the responsible use of AI across implementation practice, implementation research, and bridging use cases.

methodsAIM-IS is a multi-phase, multi-method methodological development program. The unit of analysis is the AI-for-implementation use case: a specific AI capability supporting a defined implementation practice or research task within a workflow, decision point, and governance context. Phase 1 is a living scoping review mapping published AI use cases in implementation science, including how they are evaluated and what risks they raise. Phase 2 is a qualitative interview study with implementation researchers, practitioners, AI experts, community members, and data infrastructure and governance experts to refine use cases and identify feasibility constraints, outcome priorities, and reporting needs. Phase 3 will integrate findings from Phases 1 and 2 to develop the draft AIM-IS products, including a framework, a taxonomy of use cases, guardrails for responsible use, a practical guide, outcome domains, and reporting items. Phase 4 will use an eDelphi process and consensus meeting to refine and finalize these products. Phase 5 will conduct usability testing to improve clarity and ease of use, resulting in the finalized AIM-IS products. AIM-IS is informed by implementation science, sociotechnical systems, equity, and responsible AI frameworks, and includes a living-update approach to support ongoing refinement. DISCUSSION: The AIM-IS program will deliver a suite of products, including a framework, toolkit and reporting standard, to support the specification, governance, evaluation, and reporting of AI in implementation science. Together, these products aim to strengthen transparency, comparability, accountability, and attention to equity in how AI is used by implementation practitioners and researchers over time. REGISTRATION: Open Science Framework, March 15, 2026: https://doi.org/10.17605/OSF.IO/BX35K.

Indexed as

Artificial IntelligenceImplementation ScienceHumansArtificial intelligenceGenerative AIImplementation practiceImplementation researchLarge language modelsMachine learningMethodologyReporting guideline

Identifiers

PMID41975498
PMCPMC13192042

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.