ArticleJMIR research protocols2026
Intersectionality in AI and Machine Learning for Health Care: Protocol for a Scoping Review.
Article in JMIR research protocols, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Intersectionality in AI and Machine Learning for Health Care: Protocol for a Scoping Review.JMIR research protocols · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAI 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.
objectiveThis 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.
methodsThe 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.
resultsPreliminary searches yielded a total of 4191 records across 6 databases. The scoping review will be completed by October 2026.
conclusionsThis 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. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/103601.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.