Evidence map›Paper›PMID 40528866›Full record

ArticleJournal of the American College of Emergency Physicians open2025

Trust of Artificial Intelligence-Augmented Point-of-Care Ultrasound Among Pediatric Emergency Physicians.

Margaret Lin-Martore, Aaron Kornblith, Maytal Firnberg, Adnan Haque, Bridget O'Brien

Abstract read
In one paragraph

Article in Journal of the American College of Emergency Physicians open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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

  1. Pooled it
  2. Article
  3. Review
  4. AI-Enhanced POCUS in Emergency Care.Diagnostics (Basel, Switzerland) · 2026
    Review
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.

Margaret Lin-MartoreDepartments of Emergency Medicine and Pediatrics, University of California, San Francisco, California, USA.
Aaron KornblithDepartments of Emergency Medicine and Pediatrics, University of California, San Francisco, California, USA.
Maytal FirnbergDepartments of Emergency Medicine and Pediatrics, University of California, San Francisco, California, USA.
Adnan HaqueDepartments of Emergency Medicine and Pediatrics, University of California, San Francisco, California, USA.
Bridget O'BrienDepartment of Medicine, University of California, San Francisco, California, USA.

Funding

Accurate and Reliable Diagnostics for Injured Children: Machine Learning for UltrasoundK23HD110716 · NICHD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Aaron Edward Kornblith · 2023 to 2026
$650k
NICHD NIH HHS K23 HD110716
6 · The paper itself

Abstract

Objectives: Artificial intelligence (AI) may improve many aspects of point-of-care ultrasound (POCUS) for physicians. However, adoption of AI relies on physician trust. Our study seeks to understand factors influencing physician trust in AI-augmented POCUS. Methods: From November 2023 to April 2024, we conducted semistructured interviews with academic pediatric emergency medicine (PEM) physicians who use POCUS. The interview guide was sensitized by Yang's proposed framework for user trust in AI and explored participants' perspectives on AI-augmented POCUS. We used template analysis to identify themes. Interviews continued until thematic sufficiency was achieved. Results: We interviewed 14 PEM physicians across career stages with varying POCUS experience. Participants named several specific aspects of POCUS where AI would be beneficial including image acquisition, image interpretation, and workflow enhancement. We identified themes related to 5 factors perceived as influential in physicians' trust in AI-augmented POCUS: (1) technological-AI-augmented POCUS must be reliable, accurate, transparent, and overridable by the physician; (2) contextual-The technology should be generalizable to the clinical population; (3) user-Each physician's clinical experience, comfort with POCUS, and experiences with technology affect their trust; (4) social/organizational-Endorsements by known colleagues, institutions, and national groups can engender trust although the organization or company creating the AI may have variable influence; (5) environmental-AI is everywhere, and there is subconscious use and acceptance. Conclusion: Technological, contextual, user-related, social, organizational, and environmental factors influenced emergency physician trust in AI-augmented POCUS. Understanding these factors is important for developing AI-augmented POCUS tools.

Indexed as

artificial intelligencepediatric emergency medicinepoint-of-care ultrasoundqualitative study

Identifiers

PMID40528866
PMCPMC12172972

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.