Evidence map›Paper›PMID 41513291›Full record

ArticleBMJ health & care informatics2026

Online survey assessing US primary care physicians' attitudes toward AI use in clinical administrative tasks.

Bohye Kim, Katie Ryan, Max Kasun, Laura Weiss Roberts, Jane Kim

Abstract read
In one paragraph

Article in BMJ health & care informatics, 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

5 authors.

Bohye KimDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California, USA.ORCID http://orcid.org/0000-0003-2273-1675
Katie RyanDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California, USA.ORCID http://orcid.org/0000-0003-1437-3126
Max KasunDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California, USA.ORCID http://orcid.org/0000-0002-6364-6234
Laura Weiss RobertsDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California, USA.
Jane KimDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California, USA janepkim@stanford.edu.ORCID http://orcid.org/0000-0002-1475-9822

Funding

Stakeholder Guidance to Anticipate and Address Ethical Challenges in Applications of Machine Learning and Artificial Intelligence in Algorithmic Medicine: a Novel Empirical ApproachR01TR003505 · NCATS · STANFORD UNIVERSITY · PI KIM, JANE PAIK · 2020 to 2023
$1.8M
NCATS NIH HHS R01 TR003505
6 · The paper itself

Abstract

objectivesTo examine primary care physicians' attitudes regarding artificial intelligence (AI) use for administrative clinical tasks.

methodsWeb-based survey with US physicians in family medicine or internal medicine (N=420, response rate 5.13%). Two hypothetical AI tools for administrative clinical activities were described. We examined physicians' attitudes towards AI tools, and their associations with practice years, exposure to AI, use case and stakeholder type were evaluated using generalised estimating equations.

resultsParticipants were on average 49.6 years (SD=12.5) and 56.7% men (238/420). Physicians with fewer practice years were more likely to endorse the tools' benefits (OR 1.70-1.96), the tools' benefits outweighing risks (OR 1.79-2.06) and their openness to use (OR 1.63-1.83), and were less likely to endorse disclosure of AI use (OR 0.60 (95% CI 0.36 to 0.998)). Physicians with AI exposure were more likely to agree the tools' benefits outweighed their risks (OR 1.51 (95% CI 1.06 to 2.16)). Physicians were more likely to endorse the tools' benefit to physicians (OR 4.94 (95% CI 4.16 to 5.86)) and physicians' openness to using them (OR 3.53 (95% CI 2.97 to 4.20)) than they were to endorse their benefit to patients and patients' openness. Physicians rated an AI tool for notes generation as more beneficial than one for billing assistance (OR 1.73 (95% CI 1.39 to 2.16)). DISCUSSION: Although the findings are preliminary, US primary care physicians' attitudes toward AI for clinical administration varied by practice years, prior exposure to AI, use case and stakeholder type.

conclusionOur findings highlight opportunities to develop training and implementation strategies in service of advancing safe and effective integration of administrative AI tools in primary care.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelPhysicians, Primary CareAdultFemaleHumansMaleMiddle AgedSurveys and QuestionnairesUnited StatesArtificial intelligencePrimary Health Care

Identifiers

PMID41513291
PMCPMC12815081

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.