Evidence map›Paper›PMID 42290933›Full record

ArticleJAMIA open2026

Patient perspectives on clinicians' use of ambient AI scribes.

Shreya J Shah, Kirsten Naumann Murtagh, Danton Char, Yejin Jeong, Trevor Crowell, Margaret Smith, Stephen P Ma, April S Liang, Bethel Mieso, Clarissa Delahaie and 10 more

Abstract read
PubMed Full text at PMC Publisher
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. 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

20 authors.

Shreya J ShahDepartment of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, United States.
Kirsten Naumann MurtaghDivision of Primary Care and Population Health, Stanford University School of Medicine, Stanford Healthcare AI Applied Research Team, Stanford, CA, 94305, United States.
Danton CharDepartment of Anesthesia, Stanford University School of Medicine, Stanford, CA, 94305, United States.
Yejin JeongDivision of Primary Care and Population Health, Stanford University School of Medicine, Stanford Healthcare AI Applied Research Team, Stanford, CA, 94305, United States.
Trevor CrowellDivision of Primary Care and Population Health, Stanford University School of Medicine, Stanford Healthcare AI Applied Research Team, Stanford, CA, 94305, United States.
Margaret SmithDivision of Primary Care and Population Health, Stanford University School of Medicine, Stanford Healthcare AI Applied Research Team, Stanford, CA, 94305, United States.
Stephen P MaDepartment of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, United States.ORCID https://orcid.org/0000-0003-3738-9569
April S LiangDepartment of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, United States.ORCID https://orcid.org/0009-0002-3890-4104
Bethel MiesoDepartment of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, United States.
Clarissa DelahaieTechnology and Digital Solutions, Stanford Medicine, Stanford, CA, 94305, United States.
Caroline HsiaTechnology and Digital Solutions, Stanford Medicine, Stanford, CA, 94305, United States.
Nick WaggonerTechnology and Digital Solutions, Stanford Medicine, Stanford, CA, 94305, United States.
Tait ShanafeltDepartment of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, United States.ORCID https://orcid.org/0000-0002-7106-5202
Alpa VyasOffice of Patient Experience, Stanford Health Care, Palo Alto, CA, 94305, United States.
Joshua FrostOffice of Patient Experience, Stanford Health Care, Palo Alto, CA, 94305, United States.
Mystique Smith-BentleyOffice of Patient Experience, Stanford Health Care, Palo Alto, CA, 94305, United States.
Michael A PfefferDepartment of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, United States.
Christopher SharpDepartment of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, United States.
Steven LinDepartment of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, United States.
Patricia GarciaDepartment of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study assessed over 2000 patient perspectives on the use of ambient AI scribes in outpatient visits. Materials and Methods: This prospective quality improvement study was conducted at Stanford Health Care between May and July 2025. Outcome measures included patient perceived helpfulness of ambient AI scribes and patient interest in future use. Results: Among 2202 survey respondents, 70.1% patients found the ambient AI scribe helpful and 73.6% preferred future use of ambient AI scribes. Small but statistically significant differences were observed across gender, age, and race. Discussion: In an evaluation of patient perceptions of an ambient AI scribe integrated into clinical practice, the majority of patients who had experienced the technology found it acceptable, and most found the tool helpful and desired use with future visits. Conclusion: These results suggest that ambient AI scribes may be acceptable to many patients in clinical practice. Further research is needed to inform patient-centered design and workflow improvements.

Indexed as

ambient intelligenceambient scribesArtificial intelligencedocumentationelectronic health recordsgenerative artificial intelligenceinformaticslarge language models

Identifiers

PMID42290933
PMCPMC13264434

What OpenQuestion holds

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LicenceCC BY
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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.