Intersectionality in AI and Machine Learning for Health Care: Protocol for a Scoping Review.

Authors

Duy A Dinh, Julia St Louis, Erin Ziegler, Patricia Gongal, Katie L Bain, Ani Orchanian-Cheff, Sandra Holdsworth, Shilpa Raju, Divya Sharma, Mamatha Bhat, Kathleen A Sheehan, Suze Berkhout

Published in

JMIR research protocols. Volume 15. Pages e103601. Sep 30, 2026. Epub Sep 30, 2026.

Abstract

AI and machine learning (ML) are increasingly being used in health care settings but may reproduce or exacerbate systemic biases. The integration of intersectionality and equity-related concepts offers an analytical framework to guide more reflexive, equity-oriented AI and ML design and implementation, particularly when paired with a coproduction philosophy that meaningfully includes community-based collaborators and knowledge users. However, there is limited synthesis on how intersectionality is conceptualized and operationalized in this context, and the extent to which interdisciplinary collaborators and patient partners are involved in such undertakings is unknown.
This study aims to synthesize how intersectionality is conceptualized and operationalized, including frameworks, pedagogical tools, and methods, in AI and ML for health care, as well as the extent to which such projects use participatory or coproduction frameworks.
The proposed scoping review will be conducted in accordance with the scoping review framework developed by the Joanna Briggs Institute. With the assistance of a research librarian, the following databases will be searched for published articles with primary data: MEDLINE, Embase, Emcare Nursing, APA PsycInfo, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials. Eligible studies will include any primary article that applies intersectionality in guiding the design and implementation of AI and ML in health care. This review will only include studies written in English. Two independent reviewers will screen the title and abstracts of articles, followed by its full-text review, for eligibility against a priori inclusion criteria. Conflicts regarding inclusion or exclusion will be resolved through consensus. Data will be extracted from the included studies and summarized narratively, supplemented by tables and charts. Patient or family partners will be engaged throughout the review process to refine the scope of the review, interpret findings, and support knowledge translation efforts, ensuring outputs are equity oriented and responsive to community priorities.
Preliminary searches yielded a total of 4191 records across 6 databases. The scoping review will be completed by October 2026.
This scoping review will map how intersectionality is used in AI and ML research in health care, with a particular focus on how the term is conceptualized, operationalized, and used in the context of community participatory practice. Findings from the review will identify key gaps in the literature and provide community-relevant recommendations on how to meaningfully integrate intersectionality into the development of AI and ML for health care.
DERR1-10.2196/103601.

PMID:
42814478
Bibliographic data and abstract were imported from PubMed on 01 Oct 2026.

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