JMIR Publications

ABSTRACT

Background:

Artificial intelligence (AI) and machine learning (ML) are being increasingly leveraged in health care settings, but risk reproducing systemic biases. 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 co-production philosophy that meaningfully includes community-based collaborators and end-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.

Objective:

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 utilize participatory or co-production frameworks.

Methods:

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: Ovid MEDLINE, Ovid Embase, Ovid Emcare Nursing, APA PsycINFO (Ovid), Cochrane Database of Systematic Reviews (Ovid), and Cochrane Central Register of Controlled Trials (Ovid). Two independent reviewers will screen the title and abstracts of articles, followed by its full text review, for eligibility against a priori inclusion criteria. Inclusion/exclusion conflicts will be resolved through a consensus process. Data will be extracted from included studies, and will be summarized narratively, accompanied by tables and charts. Patient/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.

Results:

Preliminary searches yielded a total of 4191 records across six databases. The scoping review will be completed by October 2026.

Conclusions:

This scoping review will be the first to 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 utilized in the context of community participatory practice. Findings from the review will identify key gaps in literature and provide community-relevant recommendations on how to meaningfully integrate intersectionality into the development of AI and ML for health care.