Evidence map›Paper›PMID 36913061›Full record

ArticleEmergency radiology2023

A survey of ASER members on artificial intelligence in emergency radiology: trends, perceptions, and expectations.

Anjali Agrawal, Garvit D Khatri, Bharti Khurana, Aaron D Sodickson, Yuanyuan Liang, David Dreizin

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Emergency radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

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

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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

6 authors.

Anjali AgrawalNew Delhi operations, Teleradiology Solutions, Delhi, India.ORCID http://orcid.org/0000-0002-6725-7500
Garvit D KhatriNuclear Medicine, Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA.
Bharti KhuranaEmergency Radiology, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Aaron D SodicksonEmergency Radiology, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Yuanyuan LiangEpidemiology & Public Health, University of Maryland School of Medicine, Baltimore, MD, USA.
David DreizinTrauma and Emergency Radiology, Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, Baltimore, MD, USA. daviddreizin@gmail.com.ORCID http://orcid.org/0000-0002-0176-0912

Funding

Machine learning-based segmentation and risk modeling for real-time prediction of major arterial bleeding after pelvic fracturesK08EB027141 · NIBIB · UNIVERSITY OF MARYLAND BALTIMORE · PI DREIZIN, DAVID · 2019 to 2022
$745k
NIBIB NIH HHS K08 EB027141
6 · The paper itself

Abstract

purposeThere is a growing body of diagnostic performance studies for emergency radiology-related artificial intelligence/machine learning (AI/ML) tools; however, little is known about user preferences, concerns, experiences, expectations, and the degree of penetration of AI tools in emergency radiology. Our aim is to conduct a survey of the current trends, perceptions, and expectations regarding AI among American Society of Emergency Radiology (ASER) members.

methodsAn anonymous and voluntary online survey questionnaire was e-mailed to all ASER members, followed by two reminder e-mails. A descriptive analysis of the data was conducted, and results summarized.

resultsA total of 113 members responded (response rate 12%). The majority were attending radiologists (90%) with greater than 10 years' experience (80%) and from an academic practice (65%). Most (55%) reported use of commercial AI CAD tools in their practice. Workflow prioritization based on pathology detection, injury or disease severity grading and classification, quantitative visualization, and auto-population of structured reports were identified as high-value tasks. Respondents overwhelmingly indicated a need for explainable and verifiable tools (87%) and the need for transparency in the development process (80%). Most respondents did not feel that AI would reduce the need for emergency radiologists in the next two decades (72%) or diminish interest in fellowship programs (58%). Negative perceptions pertained to potential for automation bias (23%), over-diagnosis (16%), poor generalizability (15%), negative impact on training (11%), and impediments to workflow (10%).

conclusionASER member respondents are in general optimistic about the impact of AI in the practice of emergency radiology and its impact on the popularity of emergency radiology as a subspecialty. The majority expect to see transparent and explainable AI models with the radiologist as the decision-maker.

Indexed as

Artificial IntelligenceRadiologyHumansMotivationRadiologistsSurveys and QuestionnairesUnited StatesArtificial intelligenceComputer-aided detectionEmergencyEmergency radiologyImagingMachine learningRadiologySurveyTrauma

Identifiers

PMID36913061
PMCPMC10362990

What OpenQuestion holds

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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.