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

Area of Science:

• Health Informatics
• Artificial Intelligence
• Public Health

Background:

• Persistent health disparities affect Alaska Native and American Indian (AN/AI) populations.
• Artificial intelligence and machine learning (AI/ML) offer potential solutions for improving AN/AI healthcare.
• The Alaska Tribal Health System (ATHS) seeks to leverage AI/ML for community well-being.

Purpose of the Study:

• To convene stakeholders for facilitated discussions on AI/ML in AN/AI healthcare.
• To promote Tribal sovereignty in AI/ML implementation within the ATHS.
• To foster technical capacity and centralized guidance for AI/ML research and application.

Main Methods:

• A symposium was held in Anchorage, Alaska, with 31 participants.
• Facilitated conversations involved various stakeholders and specialists.
• Small group discussions covered AI/ML applications, concerns, policies, and trust-building.

Main Results:

• Key themes included data democratization and AI/ML development under data sovereignty principles.
• Distinctions between AI/ML for quality improvement and research were highlighted.
• Collaborative partnerships are crucial for AI/ML optimization in AN/AI health.

Conclusions:

• AI/ML utilization in AN/AI healthcare requires community-defined benefits and Tribal guidance.
• Establishing trust and clear policies is essential for effective AI/ML adoption.
• The symposium marked a significant step toward responsible AI/ML integration in the ATHS.