Health AI Central
Peer-Reviewed Publication
JMIR Res Protoc2026;15e103601.September 30, 2026Journal Article

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

Duy A Dinh1, Julia St Louis2, Erin Ziegler3, Patricia Gongal4,5, Katie L Bain5, Ani Orchanian-Cheff6, Sandra Holdsworth5, Shilpa Raju5, Divya Sharma7,8, Mamatha Bhat9,10,11, Kathleen A Sheehan12,13, Suze Berkhout12,13
1Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
2Lawrence S. Bloomberg Faculty of Nursing, University of Toronto, Toronto, ON, Canada.
3Daphne Cockwell School of Nursing, Toronto Metropolitan University, Toronto, ON, Canada.
4University of Alberta, Edmonton, AB, Canada.
5Canadian Donation and Transplantation Research Program, Edmonton, AB, Canada.
6Library and Information Services, University Health Network, Toronto, ON, Canada.
7Department of Mathematics and Statistics, York University, Toronto, ON, Canada.
8Department of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
9Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.
10Division of Gastroenterology, Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
11Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada.
12Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
13Centre for Mental Health, University Health Network, Toronto, ON, Canada.
PubMed DOI

Abstract

BACKGROUND: 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 c…

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