Reclaiming Data, Restoring Health: The Indigenous Biomedical Data Science Renaissance
Indigenous Peoples are leading a renaissance in biomedical data science. Throughout this narrative review, we highlight advancements from leaders in this renaissance across four phases of biomedical data science: ethics and values that inform research, data harmonization, model development and assessment, and commercialization. In addition, we synthesize five teachings that advance biomedical data science: Indigenous sovereignty, ethical stewardship, relationality and trust, community prioritization, and Indigenous worldviews. We conclude with considerations for all biomedical data scientists, Indigenous People in biomedical data science, and Indigenous leaders leveraging biomedical data science to advance Indigenous health and well-being. This renaissance offers a road map for global biomedical systems to evolve beyond colonial legacies toward practices that uphold self-determination, restore balance, embody relationality, and promote the well-being of human and nonhuman kin.
Authors
- Kelle Dhein (ORCID: https://orcid.org/0000-0002-1009-0615)
- Dornell Pete
- Alec J. Calac (ORCID: https://orcid.org/0000-0003-1348-9717)
- Danner Peter (ORCID: https://orcid.org/0009-0006-5513-9845)
- Karina Hernandez-Hernandez
- Valentín Q. de la Sierra
- Kimiora Henare
- Krystal S. Tsosie
- Oliver J. Bear Don't Walk IV
- Lauren W. Yowelunh McLester-Davis
Institutions
- Johns Hopkins University (US)
- University of Auckland (NZ)
- University of San Diego (US)
- University of Washington Medical Center (US)
- University of California San Diego (US)
- Fred Hutch Cancer Center (US)
- Institute for Urban Indigenous Health (AU)
- Arizona State University (US)
- Oklahoma State University Center for Health Sciences (US)
Publication Details
- Journal
- Annual Review of Biomedical Data Science
- Published
- 2026-05-28
- DOI
- https://doi.org/10.1146/annurev-biodatasci-092524-121351
- Primary Topic
- Indigenous Health, Education, and Rights
- Type
- article
- Field-Weighted Citation Impact
- 0.00