Artificial intelligence/machine learning in Alaska Native healthcare systems
Symposium perspectives
Bibliographic Data
| ID | 24229751 |
|---|---|
| Authors | Vanessa Hiratsuka (0000-0002-4234-9441, Southcentral Foundation, University of Alaska Anchorage), Angelica Pritchard (Stanford University), Gretchen Day (Alaska Native Tribal Health Consortium, Research Services), Timothy Thomas (Alaska Native Tribal Health Consortium, Research Services), Kyle Wark (0000-0001-7640-6697, Southcentral Foundation), Aliassa Shane (Southcentral Foundation), Robert Onders (Maniilaq Association, Health Services), Tina Hernandez-Boussard (Stanford University), Brian Rice (0000-0002-5416-8058, Stanford University, Emergency Medicine) |
| Year | 2026 |
| Volume | 85 |
| Issue | 1 |
| Pages | 2677426-2677426 |
| Publication date | 2026-12-31 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Circumpolar Health (JOURNAL) |
| Journal identifiers | ISSN: 1239-9736 • E-ISSN: 2242-3982 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/22423982.2026.2677426 |
| PMID | 42176020 |
| OpenAlex | W7162193268 |
| Language | EN |
| References cited | 25 |
Artificial intelligence and machine learning (AI/ML) utilisation in Alaska Native and American Indian (AN/AI) healthcare presents an opportunity to address persistent health disparities and improve the well-being of Alaska Native communities. The 2025 Artificial Intelligence in Alaska Native Health Care Systems symposium occurred in Anchorage, Alaska, with 31 individuals participating in facilitated conversations between various stakeholders and specialists at the Alaska Native Tribal Health Consortium, Southcentral Foundation, Maniilaq Association and Stanford University. The overarching goal of the symposium was to bring together partners within the Alaska Tribal Health System (ATHS) to promote: (1) AN/AI peoples’ guidance in support of Tribal sovereignty for AI/ML implementation within the ATHS, (2) expanded technical capacity and (3) centralised guidance and expertise to lead future AI/ML research and implementation with community-defined benefits. During the event, small group discussions focused on the following topics: (1) common AI/ML applications, (2) concerns surrounding AI/ML, (3) current or potential policies guiding AI/ML use and (4) how to build trust in AI/ML models. Themes discussed were data democratisation, AI/ML development and use needs to occur under the principles of data sovereignty, and AI/ML quality improvement and research are needed and distinct. The symposium was an important step in considering how to use AI/ML to optimise AN/AI health and well-being through collaborative partnerships and services.
AI policy · Alaska Native health · data sovereignty · health disparities · health system capacity building · Indigenous health research · Machine learning · Native American health · tribal sovereignty · Artificial Intelligence in Healthcare · Artificial Intelligence in Healthcare and Education · Ethics and Social Impacts of AI · Indigenous Health, Education, and Rights
| Highly cited | No |
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