ArticleAI and ethics2026
Community perspectives on health AI: hopes, concerns and implications for health systems and trustworthy AI.
Article in AI and ethics, 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.
- Engaging Arab/MENA Communities in Learning Health Systems: Insights and Guidance for Future Research on AI and Health Equity.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
11 authors.
Funding
Abstract
The increasing use of artificial intelligence (AI) in healthcare has heightened public focus on issues of trust, transparency, and governance. In this study, we conducted five virtual community deliberations with Michigan residents (n = 159) to explore their hopes, concerns, and perspectives on how to promote trustworthiness in health AI. Participants were predominantly female (65%) with a mean age of 46 years, including 35% African American, 33% White, and 21% Middle Eastern/North African residents, with 40% reporting incomes below $50,000. Participants recognized AI's potential to improve diagnostic accuracy, efficiency, and access to health information, but voiced concerns about privacy, lack of transparency, diminished human interaction, and insufficient oversight. They recommended clear communication about when and how AI tools are used; regulation and oversight that includes patient and community input; and the use of AI to augment, not replace, human judgment and empathy. These findings informed the development of practice and policy recommendations for trustworthy health AI frameworks emphasizing human-centeredness, transparency, oversight, and accountability, underscoring the critical role of public involvement in AI governance. Future research should engage diverse community perspectives when developing health AI tools to support safe, trustworthy implementation in healthcare. Supplementary Information: The online version contains supplementary material available at 10.1007/s43681-026-00987-7.
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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.