Evidence map›Paper›PMID 41768100›Full record

ArticleAI and ethics2026

Community perspectives on health AI: hopes, concerns and implications for health systems and trustworthy AI.

Kerry A Ryan, Morgan L Sielaff, Dalya Saleem, Joshua Richardson, Sean Tan, Reema Hamasha, Paige Nong, Sharon L R Kardia, Veronica Romanov, Adnan Hammad and 1 more

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Kerry A RyanUniversity of Michigan Medical School, Ann Arbor, USA.
Morgan L SielaffUniversity of Michigan Medical School, Ann Arbor, USA.
Dalya SaleemUniversity of Michigan Medical School, Ann Arbor, USA.
Joshua RichardsonUniversity of Michigan, Ann Arbor, USA.
Sean TanUniversity of Michigan Medical School, Ann Arbor, USA.
Reema HamashaUniversity of Michigan Medical School, Ann Arbor, USA.
Paige NongUniversity of Minnesota School of Public Health, Minneapolis, USA.
Sharon L R KardiaUniversity of Michigan School of Public Health, Ann Arbor, USA.
Veronica RomanovUniversity of Michigan, Ann Arbor, USA.
Adnan HammadUniversity of Michigan, Ann Arbor, USA.
Jodyn PlattUniversity of Michigan Medical School, Ann Arbor, USA.

Funding

Public trust of artificial intelligence in the precision CDS health ecosystem - Administrative SupplementR01EB030492 · NIBIB · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI PLATT, JODYN ELIZABETH · 2021 to 2024
$3.1M
NIBIB NIH HHS R01 EB030492
6 · The paper itself

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.

Indexed as

Artificial intelligenceCommunity participationHealthcareHealth policyPublic deliberationTrust

Identifiers

PMID41768100
PMCPMC12945899

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.