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PERSPECTIVE article
Front. Artif. Intell.
Sec. Medicine and Public Health
Volume 8 - 2025 | doi: 10.3389/frai.2025.1568886
This article is part of the Research Topic Ethical and Legal Implications of Artificial Intelligence in Public Health: Balancing Innovation and Privacy View all 4 articles
Community-engaged artificial intelligence: An upstream, participatory design, development, testing, validation, use and monitoring framework for artificial intelligence and machine learning models in the Alaska Tribal Health System
Provisionally accepted
Brian Rice 1*
Stacy Rasmus 2
Robert Onders 3
Timothy Thomas 4
Gretchen Day 4 Jeremy Wood 3 Carla Britton 4
Tina Hernandez-Boussard 1
Vanessa Hiratsuka 5- 1 Stanford University, Stanford, United States
- 2 University of Alaska Fairbanks, Fairbanks, Alaska, United States
- 3 Maniilaq Association, Kotzebue, Alaska, United States
- 4 Alaska Native Tribal Health Consortium, Anchorage, Alaska, United States
- 5 Southcentral Foundation, Anchorage, Alaska, United States
American Indian and Alaska Native (AI/AN) communities are at a critical juncture in health research, where combining participatory methods with advancements in artificial intelligence and machine learning (AI/ML) can promote equity. Community-based participatory research methods which emerged to help Alaska Native communities navigate the complicated legacy of historical research abuses provide a framework to allow emerging AI/ML technologies to align with their unique world views, community strengths, and health care goals. A consortium of researchers (including Alaska Native Tribal Health Consortium,
Keywords: American Indian and Alaska Native (AIAN), community engaged research, Artificial in telligence, Ethical Considerations in AI, Medevacs, Emergency care, Rural Health, mixed methods
Received: 30 Jan 2025; Accepted: 21 Mar 2025.
Copyright: © 2025 Rice, Rasmus, Onders, Thomas, Day, Wood, Britton, Hernandez-Boussard and Hiratsuka. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Brian Rice, Stanford University, Stanford, United States
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