ArticleLearning health systems2026
Engaging Arab/MENA Communities in Learning Health Systems: Insights and Guidance for Future Research on AI and Health Equity.
Article in Learning health systems, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Catching the Wave: AI In Health Services Research.Learning health systems · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
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 ( 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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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.