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Engaging Arab/MENA Communities in Learning Health Systems: Insights and Guidance for Future Research on AI and Health
Reema Hamasha1, Dalya Saleem1, Kerry A Ryan2
1Department of Learning Health Sciences University of Michigan Medical School Ann Arbor Michigan USA.
Introduction:
The integration of artificial intelligence (AI) tools in healthcare offers significant opportunities to improve patient care. However, underrepresented groups such as the Arab/Middle Eastern North African (MENA) community in the United States have historically been excluded in health data and conversations regarding AI tool implementation and development. We present our experience with the Arab/MENA community exploring attitudes about the use of AI in healthcare, focusing on our engagement and recruitment efforts as well as relevance for learning health systems science.
Methods:
We conducted a virtual democratic deliberation session (n = 33) in partnership with the Arab Community Center for Economic and Social Services (ACCESS) in Michigan, as part of a larger study involving five sessions (n = 159). In partnership with ACCESS staff, we collaboratively developed study materials and recruited Arab/MENA community members to share their perspectives on AI in healthcare. Qualitative thematic analysis was used to identify the community's perspectives, priorities, and barriers to the use of AI in healthcare.
Results:
The deliberation session highlighted four key themes related to the use of AI tools: (1) transparency in AI development was viewed as essential to building community trust, (2) human connection, with concerns that increased reliance on AI could replace empathy and weaken patient-provider interactions, (3) the role of healthcare providers, with preference on providers using AI as a supportive tool rather than replacing direct care, and (4) representation due to concerns over whether AI systems would reflect the experiences and needs of the Arab/MENA community in healthcare.
Conclusions:
Community-based partnerships are essential for advancing responsible AI implementation in healthcare and building a learning health system. Our experiences highlight the importance of transparency, cultural sensitivity, and meaningful community involvement to build trust and address the needs of underrepresented groups as AI evolves.
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