A survey of ASER members on artificial intelligence in emergency radiology: trends, perceptions, and expectations
Open accessAbstract
Purpose There 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.Methods An 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.Results A 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%).Conclusion ASER 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.
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A facilitating factor is that outcomes and clinical effectiveness of AI applications should be evidence-based, ideally relying on large randomized controlled trials to support claims about effectiveness and safety, while the development process itself should be transparent and the AI system used should be explainable and verifiable.
“Hereby, a facilitating factor is that the outcomes and clinical effectivenes of AI applications should be evidence-based, ideally relying on large randomized controlled trials (RCTs) to support claims about its effectiveness and safety [36, 38, 39, 42, 58], while the development process itself should be transparent [63] and the AI system used should be explainable and verifiable [63].”
From Healthcare professionals’ perspectives on artificial intelligence in patient care: a systematic review of hindering and facilitating factors on different levels (2025) · cites this paper as [63]
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- Authors
- Agrawal, A., Khatri, G. D., Khurana, B., Sodickson, A. D., Liang, Y., & Dreizin, D.
- Affiliations
- University of Washington (US)
- Brigham and Women's Hospital (US)
- University of Maryland, Baltimore (US)
- Corresponding author
- Dreizin, David
- ORCID
- Venue
- Emergency Radiology
- Published
- 2023-03-13
- Volume
- 30
- Issue
- 3
- Pages
- 267-277
- ISSN
- 1070-3004 (ISSN-L)
- 1438-1435
- PMC
- PMC10362990
- OpenAlex
- W4324017600
- Cited by
- 41
- References
- 65
- Citations by year
- 2026: 9 · 2025: 12 · 2024: 13 · 2023: 7
- FWCI
- 1.63
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Emergency Radiology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Foundation for the National Institutes of Health (NIH K08 EB027141-01A1)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- artificial intelligence in radiology, radiologist perception, clinical decision support, computer-aided detection, explainable AI, AI transparency, automation bias, overdiagnosis, radiologist workforce, clinical implementation of AI
- MeSH terms
- Radiologists, Artificial Intelligence, Humans, Motivation, Surveys and Questionnaires, Radiology, United States
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