AI Methods for Implementation Science (AIM-IS)

2026 Preprint Open access

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Authors: Guillaume Fontaine, Susan Michie, Rinad S. Beidas, Elvin Geng, Christine Fahim, Byron J. Powell, Vivian Andrea Welch, James Thomas, J. Chan, Samira Abbasgholizadeh-Rahimi, France Légaré, Janna Hastings
+6 more Sylvie Lambert, Justin Presseau, Sharon E. Straus, Ruopeng An, Ashrita Saran, Natalie Taylor
DOI: 10.17605/osf.io/ju8mh
Cited by: 0

Abstract

AIM-IS 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 researchers and practitioners over time.

Fields of Study & Subjects

Health Policy Implementation Science Artificial Intelligence in Healthcare and Education Ethics and Social Impacts of AI

Publication details

Publication date
1 January 2026
Language
EN
Document type
Preprint