Evidence map›Paper›PMID 41536575›Full record

ArticleJournal of the American College of Emergency Physicians open2026

Early Insights Among Emergency Medicine Physicians on Artificial Intelligence: A National, Convenience-sample Survey of the American College of Emergency Physicians.

Bradley D Shy, Cristiana Baloescu, Isaac V Faustino, R Andrew Taylor, Michael Gottlieb, Rohit B Sangal, Colton Hood, Nicholas Genes, Elaine J Rabin, ACEP AI Task Force

Abstract read
In one paragraph

Article in Journal of the American College of Emergency Physicians open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. What Do We Mean When We Say "AI?" Why Context-Not Volume-Should Guide Emergency Medicine.Journal of the American College of Emergency Physicians open · 2026
    Article
  3. 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

10 authors.

Bradley D ShyDepartment of Emergency Medicine, University of Colorado, School of Medicine, Aurora, Colorado, USA.
Cristiana BaloescuDepartment of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut, USA.
Isaac V FaustinoDepartment of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut, USA.
R Andrew TaylorDepartment of Emergency Medicine, University of Virginia School of Medicine, Charlottesville, Virginia, USA.
Michael GottliebDepartment of Emergency Medicine, Rush University Medical Center, Chicago, Illinois, USA.
Rohit B SangalDepartment of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut, USA.
Colton HoodDepartment of Emergency Medicine, George Washington University School of Medicine and Health Science, Washington, DC, USA.
Nicholas GenesRonald O. Perelman Department of Emergency Medicine, NYU Langone Health, New York, New York, USA.
Elaine J RabinDepartment of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
ACEP AI Task Force

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: This study aimed to assess the current utilization of artificial intelligence (AI) tools among emergency physicians, their attitudes toward AI in clinical practice, and how a national physician professional organization could best support its members regarding AI. Methods: A cross-sectional survey was emailed to American College of Emergency Physicians members and made available to conference attendees at a national symposium. The survey collected demographic information, details on the use of noninstitutional and institutional AI tools, attitudes toward AI, and desired forms of support. Descriptive statistics were used to summarize the data. Results: A total of 658 physicians responded, primarily practicing attendings (78%) and residents (9%), with 60% aged 35 to 54 years and 67% identifying as male; these respondents represented 2% of the membership of American College of Emergency Physicians. Noninstitutional AI tool use (eg, ChatGPT and independent electrocardiogram interpretation) was reported by 31% of respondents. Institutional AI was integrated into 52% of respondents' practices, with 18% regularly using ambient AI documentation and 22% using AI-assisted clinical decision support. AI tools for point-of-care ultrasound were available to 10%, and AI-assisted radiology interpretation was used by 14%, mainly for X-rays and computed tomography. Operational AI for triage, capacity management, and staff optimization were reported by 15%, while 9% used AI-assisted coding and billing. Moreover, 75% believed AI improves clinical efficiency, 57% felt it enhanced care quality, but 12% expressed concern about job displacement, 16% are unsure whether AI tools would adequately comply with Health Insurance Portability and Accountability Act regulations, and 38% noted potential biases. About half desire educational support and guidelines. Conclusion: In this nonrepresentative emergency physician survey, respondents reported moderate rates of adoption of AI tools and generally positive attitudes toward AI's impact on efficiency and care quality. However, respondents also reported important concerns about job displacement, Health Insurance Portability and Accountability Act regulation compliance, and potential biases. Larger studies are needed to more fully understand emergency physician views on AI.

Indexed as

AIalgorithmic biasartificial intelligenceemergency medicineintegrationphysician attitudesprofessional support

Identifiers

PMID41536575
PMCPMC12796722

What Socratic 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 Socratic graph.