Evidence map›Paper›PMID 42851723›Full record

ArticleBMJ digital health & AI2026

Public perspectives on artificial intelligence in healthcare in the USA: a cross-sectional survey.

Vishnu Prakas Nair, Rebecca Handler, Megha D Tandel, Yingjie Weng, Laura M Holdsworth, Ethan Goh, Roger W Sands, Natasha Zr Steele, Laura T Derry, Robert D Boutin and 12 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMJ digital health & AI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

22 authors.

Vishnu Prakas NairDivision of Hospital Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA.ORCID https://orcid.org/0000-0002-0406-3987
Rebecca HandlerDepartment of Medicine, Stanford University School of Medicine, Stanford, California, USA.
Megha D TandelQuantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, California, USA.
Yingjie WengQuantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, California, USA.
Laura M HoldsworthDivision of Primary Care and Population Health, Department of Medicine, Stanford University, Stanford, California, USA.
Ethan GohStanford Division of Computational Medicine, Stanford University School of Medicine, Stanford, California, USA.ORCID https://orcid.org/0000-0003-1965-025X
Roger W SandsDivision of Gastroenterology and Hepatology and Department of Medicine, Stanford University School of Medicine, Stanford, California, USA.
Natasha Zr SteeleDivision of Hospital Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA.ORCID https://orcid.org/0000-0002-5820-5151
Laura T DerryDivision of Hospital Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA.ORCID https://orcid.org/0009-0009-1825-8038
Robert D BoutinDepartment of Radiology, Stanford University School of Medicine, Stanford, California, USA.
Matthew DeBellInstitute for Research in the Social Sciences, Stanford University, Stanford, California, USA.
Tamara ScottDivision of Hospital Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA.
David SvecDivision of Hospital Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA.
Neera AhujaDivision of Hospital Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA.ORCID https://orcid.org/0000-0002-2979-7530
Fatima RodriguezDivision of Cardiovascular Medicine and the Cardiovascular Institute, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA.
Roxana DaneshjouDepartment of Biomedical Data Science, Stanford University, Stanford, California, USA.
Akshay ChaudhariDepartment of Radiology, Stanford University School of Medicine, Stanford, California, USA.
Sigall K BellDepartment of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
Jonathan H ChenDivision of Hospital Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA.
Andre KumarDivision of Hospital Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA.
Adam RodmanShapiro Institute for Research and Education, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
Jason HomDivision of Hospital Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To evaluate public perspectives on artificial intelligence (AI) in healthcare in the USA and assess variation in trust, comfort and perceived fairness across demographic groups. Methods and analysis: This cross-sectional survey of US adults was conducted between 21 November and 4 December 2025, via YouGov, a private public opinion research firm, using a non-probability-based online panel. The sample included 500 general population respondents and an oversample of 750 adults aged 60+. Survey weights were applied to approximate the US adult population. Responses were measured using Likert-type or categorical scales and analysed using survey-weighted statistical tests with false discovery rate (FDR) correction for multiple comparisons. Primary outcomes included comfort with healthcare providers using AI, attitudes towards AI-generated second opinions, prior use of AI for healthcare, relative trust in AI versus clinicians for health information, preference for AI chatbots versus providers in explaining care plans and perceived effects of AI on healthcare fairness. Results: Among weighted respondents (n=500), 34.0% reported prior healthcare AI use, which was more common among college graduates than those without a high school diploma (46.5% vs 22.9%). Only 25.4% reported being comfortable or very comfortable with their provider using AI, while 43.0% were uncomfortable and 31.5% were neutral. Regarding information trust, 44.5% trusted AI much less than clinicians; 88.4% reported a provider would better help them understand their care plan than a chatbot. Prior AI use was associated with more favourable attitudes towards laboratory/imaging interpretation and AI-assisted referrals. Differences by political affiliation, insurance, health status and age were not statistically significant after FDR correction. Conclusions: AI implementation appears to be outpacing public trust. Higher educational attainment was the most consistent predictor of favourable attitudes. Notably, one-third of respondents were neutral across outcomes, suggesting public attitudes are potentially modifiable. These findings underscore the need for transparent deployment and targeted trust-building strategies to support equitable AI adoption in healthcare.

Indexed as

Artificial intelligenceDecision Making, Computer-AssistedDelivery of Health CareHealth CommunicationPatient-Centered Care

Identifiers

PMID42851723
PMCPMC13646972

What OpenQuestion holds

Textmetadata
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.