ArticleThe American journal of managed care2026
Building trust: public priorities for health care AI labeling.
Article in The American journal of managed care, 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
6 authors.
Funding
Abstract
objectivesLabeling and the use of model cards have been promoted as ways to increase transparency for multiple end users. This study aimed to identify key content for a health artificial intelligence (AI) tool label based on public perspectives and expectations. STUDY
designWe used a mixed-methods study design, combining public deliberation and pre-/post surveys to inform participants about AI in health care and gather input on key information for a health AI tool label.
methodsIn 2024, we conducted 5 virtual community deliberations across Michigan, engaging 159 participants in facilitated small-group discussions that were qualitatively coded. Participants completed a 20-minute survey before and after the deliberation to assess changes in knowledge, attitudes, and trust regarding AI in health care.
resultsParticipants prioritized information regarding privacy and security, health equity, and safety and effectiveness of AI tools for inclusion on a health AI tool label. An AI label is, therefore, a familiar and transparent mechanism to build trust and address patients' desire for notification.
conclusionsThe findings highlight ethical gaps in using AI in health care settings and the value of publicly informed, patient-centered solutions. There is strong demand for clear, accessible information on how AI tools are used and their risks and benefits. A patient-informed label may address these ethical challenges and improve transparency, trust, and patient-centered communication as AI reshapes health care.
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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.