Evidence map›Paper›PMID 40126477›Full record

ArticleJAMA network open2025

Physician Perspectives on Ambient AI Scribes.

Shreya J Shah, Trevor Crowell, Yejin Jeong, Anna Devon-Sand, Margaret Smith, Betsy Yang, Stephen P Ma, April S Liang, Clarissa Delahaie, Caroline Hsia and 5 more

Abstract read
In one paragraph

Article in JAMA network open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
72citing papers in PubMed, 1 pooled it
–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

72 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Artificial intelligence in clinical genetics: Current practice and attitudes among the clinical genetics workforce.Genetics in medicine : official journal of the American College of Medical Genetics · 2026
    Article
  11. Effect of use of ambient listening technology on patient-reported communication and satisfaction ratings.Journal of the American Medical Informatics Association : JAMIA · 2026
    Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Artificial Intelligence Scribes in Orthopaedic Surgery: A Narrative Review.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2026
    Review
  17. Review
  18. Observational
  19. Article
  20. Article

12 more citing papers are in PubMed but not listed here.

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

15 authors.

Shreya J ShahDepartment of Medicine, Stanford University School of Medicine, Stanford, California.
Trevor CrowellStanford Healthcare AI Applied Research Team, Division of Primary Care and Population Health, Stanford University School of Medicine, Stanford, California.
Yejin JeongStanford Healthcare AI Applied Research Team, Division of Primary Care and Population Health, Stanford University School of Medicine, Stanford, California.
Anna Devon-SandStanford Healthcare AI Applied Research Team, Division of Primary Care and Population Health, Stanford University School of Medicine, Stanford, California.
Margaret SmithStanford Healthcare AI Applied Research Team, Division of Primary Care and Population Health, Stanford University School of Medicine, Stanford, California.
Betsy YangDepartment of Medicine, Stanford University School of Medicine, Stanford, California.
Stephen P MaDepartment of Medicine, Stanford University School of Medicine, Stanford, California.
April S LiangDepartment of Medicine, Stanford University School of Medicine, Stanford, California.
Clarissa DelahaieTechnology and Digital Solutions, Stanford Medicine, Stanford, California.
Caroline HsiaTechnology and Digital Solutions, Stanford Medicine, Stanford, California.
Tait ShanafeltDepartment of Medicine, Stanford University School of Medicine, Stanford, California.
Michael A PfefferDepartment of Medicine, Stanford University School of Medicine, Stanford, California.
Christopher SharpDepartment of Medicine, Stanford University School of Medicine, Stanford, California.
Steven LinDepartment of Medicine, Stanford University School of Medicine, Stanford, California.
Patricia GarciaDepartment of Medicine, Stanford University School of Medicine, Stanford, California.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Limited qualitative studies exist evaluating ambient artificial intelligence (AI) scribe tools. Such studies can provide deeper insights into ambient AI implementations by capturing lived experiences. Objective: To evaluate physician perspectives on ambient AI scribes. Design, Setting, and Participants: A qualitative study using semistructured interviews guided by the Reach, Efficacy, Adoption, Implementation, Maintenance/Practical, Robust Implementation, and Sustainability Model (RE-AIM/PRISM) framework, with thematic analysis using both inductive and deductive approaches. Physicians participating in an AI scribe pilot that included community and faculty practices, across primary care and ambulatory specialties, were invited to participate in interviews. This ambient AI scribe pilot at a health care organization in California was conducted from November 2023 to January 2024. Main Outcome and Measures: Facilitators and barriers to adoption, practical effectiveness, and suggestions for improvement to enhance sustainability. Results: Twenty-two semistructured interviews were conducted with AI pilot physicians from primary care (13 [59%]) and ambulatory specialties (9 [41%]), including physicians from community practices (12 [55%]) and faculty practices (10 [45%]). Facilitators to adoption included ease of use, ease of editing, and generally positive perspectives of tool quality. Physicians expressed positive sentiments about the impact of the ambient AI scribe tool on cognitive demand (16 of 16 comments [100%]), temporal demand (28 comments [62%]), work-life integration (10 of 11 comments [91%]), and overall workload (8 of 9 comments [89%]). Physician perspectives of the impact of the ambient AI scribe tool on their engagement with patients were mostly positive (38 of 56 comments [68%]). Barriers to adoption included limited functionality with non-English speaking patients and lack of access for physicians without a specific device. Physician perspectives on accuracy and style were largely negative, particularly regarding note length and editing requirements. Several specific suggestions for tool improvement were identified, and physicians were optimistic regarding the potential for long-term use of ambient AI scribes. Conclusion and Relevance: In this qualitative study, ambient AI scribes were found to positively impact physician workload, work-life integration, and patient engagement. Key facilitators and barriers to adoption were identified, along with specific suggestions for tool improvement. These findings suggest the potential for ambient AI scribes to reduce clinician burden, with user-centered recommendations offering practical guidance on ways to improve future iterations and improve adoption.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelDocumentationElectronic Health RecordsPhysiciansAdultCaliforniaFemaleHumansInterviews as TopicMaleMiddle AgedPilot ProjectsPrimary Health CareQualitative Research

Identifiers

PMID40126477
PMCPMC11933996

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.