A Multiparty Collaboration to Engage Diverse Populations in Community-Centered Artificial Intelligence Research

Area of Science:

• Health Informatics
• Artificial Intelligence in Healthcare
• Community-Engaged Research

Background:

• Artificial intelligence (AI) offers potential to improve health information access.
• Understanding diverse user interactions is crucial for optimizing AI health tools.
• Cross-sector collaboration is key for equitable AI health technology development.

Purpose of the Study:

• To test a cross-sector research model for AI-based health technology.
• To assess the usability and acceptability of an AI dermatology tool in a diverse community.
• To gather user feedback and identify community needs for AI health innovations.

Main Methods:

• A community-centered project involving Stanford Medicine, Google, and Santa Clara Family Health Plan.
• Recruitment of participants from community events in East San Jose, California.
• Iterative feedback process to identify technological, language, and privacy needs.

Main Results:

• Successfully engaged a diverse participant group, with over 70% preferring languages other than English.
• Identified critical community needs, including support for technology, language, and privacy.
• Demonstrated a viable model for collaboration between academia, industry, and community stakeholders.

Conclusions:

• Cross-sector collaboration is essential for developing inclusive and effective AI health technologies.
• Community-centered approaches are vital for addressing diverse user needs in AI healthcare.
• Learnings from this case study can guide future AI health innovation partnerships.