Artificial intelligence/machine learning in Alaska Native healthcare systems: symposium perspectives.

Clinical Snapshot

Key Findings

• P Value: Not applicable - qualitative study

• Effect Size: Not applicable - qualitative study

• Primary Outcome: Three main themes: data democratisation, AI/ML development under data sovereignty principles, and distinction between quality improvement and research applications

• Nnt Or Sensitivity: Not applicable - descriptive qualitative study

• Confidence Interval: Not applicable - qualitative study

Clinical Application

Limited by lack of specific implementation guidance and methodological detail Relevant for Aboriginal and Torres Strait Islander health services considering AI/ML implementation, particularly regarding data sovereignty and community-controlled research principles Indigenous healthcare systems and organizations implementing or considering AI/ML technologies

Abstract

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.