Evidence map›Paper›PMID 41852933›Full record

ArticleJAMIA open2026

Patient perspectives about deployment of artificial intelligence decision support tools in a safety-net healthcare system.

Nicholas Phelps, Patrick Vossler, Avni Kothari, Katherine Steineman, Seth Goldman, Arturo Gasga, Susan Ehrlich, Hemal Kanzaria, Julia Adler-Milstein, Jean Feng and 2 more

Abstract read
In one paragraph

Article in JAMIA open, 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

12 authors.

Nicholas PhelpsDepartment of Medicine, University of California San Francisco, San Francisco, CA 94122, United States.
Patrick VosslerDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA 94122, United States.
Avni KothariDepartment of Medicine, University of California San Francisco, San Francisco, CA 94122, United States.
Katherine SteinemanDepartment of Medicine, University of California San Francisco, San Francisco, CA 94122, United States.
Seth GoldmanDepartment of Medicine, University of California San Francisco, San Francisco, CA 94122, United States.
Arturo GasgaDepartment of Medicine, University of California San Francisco, San Francisco, CA 94122, United States.
Susan EhrlichZuckerberg San Francisco General Hospital, San Francisco, CA 94110, United States.
Hemal KanzariaZuckerberg San Francisco General Hospital, San Francisco, CA 94110, United States.
Julia Adler-MilsteinDepartment of Medicine, University of California San Francisco, San Francisco, CA 94122, United States.ORCID https://orcid.org/0000-0002-0262-6491
Jean FengDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA 94122, United States.
Paige NongDivision of Health Policy and Management, University of Minnesota Twin Cities, Minneapolis, MN 55455, United States.ORCID https://orcid.org/0000-0002-2849-9005
Lucas S ZierDepartment of Medicine, University of California San Francisco, San Francisco, CA 94122, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To assess patient awareness, trust, perceived benefits, and risks of artificial intelligence (AI) in clinical care within an urban safety-net health system. Materials and Methods: We surveyed 313 patients from November 2024 to January 2025 regarding AI awareness, trust in AI-assisted decision-making, and preferences for transparency and oversight. Quantitative analyses assessed associations between AI awareness and perceived benefit; qualitative analysis identified themes influencing trust. Results: While 84% were familiar with commercial AI, fewer than half recognized the use of AI in medical decision support. Greater AI awareness was associated with higher perceived benefit (all Discussion: This study provides one of the first assessments of patient perspectives on AI within a safety-net healthcare setting. Patients view clinical AI favorably but demand transparency and clinician involvement. Conclusions: Patient education and engagement are essential for equitable, trustworthy AI deployment.

Indexed as

artificial intelligencegenerative artificial intelligencepatient attitudespredictive methods

Identifiers

PMID41852933
PMCPMC12994689

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.